types of hypothesis in scientific research

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Researcher, Academic Writer, Web developer

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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Definition of a Hypothesis

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A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

Null Hypothesis

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Alternative Hypothesis

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Updated by Nicki Lisa Cole, Ph.D

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

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It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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7 Types of Research Hypothesis: Examples, Significance and Step-By-Step Guide

Introduction.

In any research study, a research hypothesis plays a crucial role in guiding the investigation and providing a clear direction for the research. It is an essential component of a thesis as it helps to frame the research question and determine the methodology to be used.

This article will provide a detailed analysis of research hypotheses in a thesis, highlighting their significance and qualities. It will also explore different types of research hypotheses and provide illustrative examples. Additionally, a step-by-step guide to developing research hypotheses and methods for testing and validating them will be discussed. By the end of this article, readers will have a comprehensive understanding of research hypotheses and their role in a thesis.

Understanding Research Hypotheses in a Thesis

A research hypothesis is a statement of expectation or prediction that will be tested by research. In a thesis, a research hypothesis is formulated to address the research question or problem statement . It serves as a tentative answer or explanation to the research question. The research hypothesis guides the direction of the study and helps in determining the research design and methodology.

The research hypothesis is typically based on existing theories, previous research findings, or observations. It is formulated after a thorough review of the literature and understanding of the research area. A well-defined research hypothesis provides a clear focus for the study and helps in generating testable predictions. By testing the research hypothesis, researchers aim to gather evidence to support or reject the hypothesis. This process contributes to the advancement of knowledge in the field and helps in drawing meaningful conclusions.

Significance of Research Hypotheses in a Thesis

One of the key significance of research hypotheses is that they help in organizing and structuring the research study. By formulating a hypothesis, the researcher defines the specific research question and identifies the variables that will be investigated. This helps in narrowing down the scope of the study and ensures that the research is focused and targeted.

Moreover, research hypotheses provide a framework for data collection and analysis. They guide the researcher in selecting appropriate research methods , tools, and techniques to gather relevant data. The hypotheses also help in determining the statistical tests and analysis techniques that will be used to analyze the collected data.

Another significance of research hypotheses is that they contribute to the advancement of knowledge in a particular field. By formulating hypotheses and conducting research to test them, researchers are able to generate new insights, theories, and explanations. This contributes to the existing body of knowledge and helps in expanding the understanding of a specific phenomenon or topic.

Furthermore, research hypotheses are important for establishing the validity and reliability of the research findings. By formulating clear and testable hypotheses, researchers can ensure that their study is based on sound scientific principles. The hypotheses provide a basis for evaluating the accuracy and generalizability of the research results.

In addition, research hypotheses are essential for making informed decisions and recommendations based on the research findings. They help in drawing conclusions and making predictions about the relationship between variables. This information can be used to inform policy decisions, develop interventions, or guide future research in the field.

Qualities of an Effective Research Hypothesis in a Thesis

An effective research hypothesis in a thesis possesses several key qualities that contribute to its strength and validity. These qualities are essential for ensuring that the hypothesis can be tested and validated through empirical research. The following are some of the qualities that make a research hypothesis effective:

1. Specificity: A good research hypothesis is specific and clearly defines the variables and the relationship between them. It provides a clear direction for the research and allows for precise testing of the hypothesis.

2. Testability: An effective hypothesis in research is testable, meaning that it can be empirically examined and either supported or refuted through data analysis. It should be possible to design experiments or collect data that can provide evidence for or against the hypothesis.

3. Clarity: A research hypothesis should be written in clear and concise language. It should avoid ambiguity and ensure that the intended meaning is easily understood by the readers. Clear language helps in communicating the hypothesis effectively and facilitates its evaluation.

4. Falsifiability: A strong research hypothesis is falsifiable, which means that it is possible to prove it wrong. It should be formulated in a way that allows for the possibility of obtaining evidence that contradicts the hypothesis. This is important for the scientific process as it encourages critical thinking and the exploration of alternative explanations.

5. Relevance: An effective research hypothesis is relevant to the research question and the overall objectives of the study. It should address a significant gap in knowledge or contribute to the existing body of literature. A relevant hypothesis adds value to the research and increases its significance.

6. Novelty: A good research hypothesis is original and innovative. It should propose a new idea or approach that has not been extensively explored before. Novelty in the hypothesis increases the potential for new discoveries and contributes to the advancement of knowledge in the field.

7. Coherence: An effective research hypothesis should be coherent and consistent with existing theories, concepts, and empirical evidence. It should align with the current understanding of the topic and build upon previous research. Coherence ensures that the hypothesis is grounded in a solid foundation and enhances its credibility.

8. Measurability: A research hypothesis should be measurable, meaning that it can be quantitatively or qualitatively assessed. It should be possible to collect data or evidence that can be used to evaluate the hypothesis. Measurability allows for objective testing and increases the reliability of the research findings.

By incorporating these qualities into the formulation of a research hypothesis, researchers can enhance the validity and reliability of their study.

Different Types of Research Hypotheses in a Thesis

In a thesis, there are several different types of research hypotheses that can be used to test the relationship between variables. These hypotheses provide a framework for the research and guide the direction of the study. Understanding the different types of research hypotheses is essential for conducting a comprehensive and effective thesis.

Null Hypothesis

The null hypothesis is a statement that suggests there is no significant relationship between the variables being studied. It assumes that any observed differences or relationships are due to chance or random variation. The null hypothesis is denoted as H0 and is often used as a starting point for hypothesis testing.

Alternative Hypothesis

The alternative hypothesis, also known as the research hypothesis, is a statement that suggests there is a significant relationship between the variables being studied. It contradicts the null hypothesis and proposes that the observed differences or relationships are not due to chance.

Directional Hypothesis

A directional hypothesis is a specific type of alternative hypothesis that predicts the direction of the relationship between variables. It states that there is a positive or negative relationship between the variables, indicating the direction of the effect.

Non-Directional Hypothesis

In contrast to a directional hypothesis, a non-directional hypothesis does not predict the direction of the relationship between variables. It simply states that there is a relationship between the variables without specifying the direction of the effect.

Statistical Hypothesis

A statistical hypothesis is a hypothesis that is formulated based on statistical analysis. It involves using statistical tests to determine the likelihood of the observed data occurring under the null hypothesis.

Associative Hypothesis

An associative hypothesis suggests that there is a relationship between variables, but it does not imply causation. It indicates that changes in one variable are associated with changes in another variable.

Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between variables. It suggests that changes in one variable directly cause changes in another variable.

These different types of research hypotheses provide researchers with various options to explore and test the relationships between variables in a thesis. The choice of hypothesis depends on the research question, the nature of the variables, and the available data.

Illustrative Examples of Research Hypotheses in a Thesis

To better understand research hypotheses in a thesis, let’s explore some illustrative examples. These examples will demonstrate how hypotheses are formulated and tested in different research studies.

Example 1: Hypothesis for a study on the effects of exercise on weight loss:

Null Hypothesis (H0): There is no significant difference in weight loss between individuals who engage in regular exercise and those who do not.

Alternative Hypothesis (H1): Individuals who engage in regular exercise will experience greater weight loss compared to those who do not exercise.

Example 2: Hypothesis for a study on the impact of social media on self-esteem:

Null Hypothesis (H0): There is no significant relationship between social media usage and self-esteem levels.

Alternative Hypothesis (H1): Increased social media usage is associated with lower self-esteem levels.

Example 3: Hypothesis for a study on the effectiveness of a new teaching method in improving student performance:

Null Hypothesis (H0): There is no significant difference in student performance between the traditional teaching method and the new teaching method.

Alternative Hypothesis (H1): The new teaching method leads to improved student performance compared to the traditional teaching method.

These examples highlight the structure of research hypotheses, where the null hypothesis represents no effect or relationship, while the alternative hypothesis suggests the presence of an effect or relationship. It is important to note that these hypotheses are testable and can be analyzed using appropriate statistical methods.

Step-by-Step Guide to Developing Research Hypotheses in a Thesis

Developing a research hypothesis is a crucial step in the process of conducting a thesis. In this section, we will provide a step-by-step guide to developing research hypotheses in a thesis.

Step 1: Identify the Research Topic

The first step in developing a research hypothesis is to clearly identify the research topic. This involves understanding the research problem and determining the specific area of study.

Step 2: Conduct Preliminary Research

Once the research topic is identified, it is important to conduct preliminary research to gather relevant information. This helps in understanding the existing knowledge and identifying any gaps or areas that need further investigation.

Step 3: Formulate the Research Question

Based on the preliminary research, formulate a clear and concise research question. The research question should be specific and focused, addressing the research problem identified in step 1.

Step 4: Define the Variables

Identify the variables that will be studied in the research. Variables are the factors or concepts that are being measured or manipulated in the study. It is important to clearly define the variables to ensure the research hypothesis is specific and testable.

Step 5: Predict the Relationship and Outcome

The research hypothesis should propose a link between the variables and predict the expected outcome. It should clearly state the expected relationship between the variables and the anticipated result.

Step 6: Ensure Clarity and Conciseness

A good research hypothesis should be simple and concise, avoiding wordiness. It should be clear and free from ambiguity or assumptions about the readers’ knowledge. The hypothesis should also be observable and measurable.

Step 7: Validate the Hypothesis

Before finalizing the research hypothesis, it is important to validate it. This can be done through further research, literature review , or consultation with experts in the field. Validating the hypothesis ensures its relevance and novelty.

By following these step-by-step guidelines, researchers can develop effective research hypotheses for their theses. A well-developed hypothesis provides a solid foundation for the research and helps in generating meaningful results.

Methods for Testing and Validating Research Hypotheses in a Thesis

Hypothesis testing is a formal procedure for investigating our ideas about the world. It allows you to statistically test your predictions. The usual process is to make a hypothesis, create an experiment to test it, run the experiment, draw a conclusion, and then allow other researchers to replicate the study to validate the findings. There are several methods for testing and validating research hypotheses in a thesis.

Experimental Research

One common method is experimental research, where researchers manipulate variables and measure their effects on the dependent variable.

Observational Research

Another method is observational research, where researchers observe and record data without manipulating variables. This method is often used when it is not feasible or ethical to conduct experiments.

Survey Research

Survey research is another method that involves collecting data from a sample of individuals using questionnaires or interviews . This method is useful for studying attitudes, opinions, and behaviors.

Conducting Meta-analysis

In addition to these methods, researchers can also use existing data or conduct meta-analyses to test and validate research hypotheses. Existing data can be obtained from sources such as government databases, previous studies, or publicly available datasets. Meta-analysis involves combining the results of multiple studies to determine the overall effect size and to test the generalizability of findings across different populations and contexts. Once the data is collected, researchers can use statistical analysis techniques to analyze the data and test the research hypotheses. Common statistical tests include t-tests, analysis of variance (ANOVA), regression analysis, and chi-square tests.

The choice of statistical test depends on the research design, the type of data collected, and the specific research hypotheses being tested. It is important to note that testing and validating research hypotheses is an iterative process. Researchers may need to refine their hypotheses, modify their research design, or collect additional data based on the initial findings. By using rigorous methods for testing and validating research hypotheses, researchers can ensure the reliability and validity of their findings, contributing to the advancement of knowledge in their field.

In conclusion, research hypotheses are essential components of a thesis that guide the research process and contribute to the advancement of knowledge in a particular field. By formulating clear and testable hypotheses, researchers can make meaningful contributions to their field and address important research questions. It is important for researchers to carefully develop and validate their hypotheses to ensure the credibility and reliability of their findings.

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• What Are The Types of Research Hypothesis? + [Examples]

It is vital to fully understand a hypothesis to address the types of research hypotheses. A hypothesis explains an established or known fact that has not yet been proven or validated.

Simply put, it is a statement explaining why and how a particular thing works based on philosophical assumptions and facts.

For example, a hypothesis goes like this;

A patient is likely to trust the pediatrician’s diagnosis based on the perception that the doctor is well-versed in the practice of medicine.

A hypothesis is a basis for scientific research or experiment, usually coined as a research hypothesis.

Three attributes or features measure the viability of a research hypothesis, and they are as follows.

• A research hypothesis must be specific, testable or measurable, and verifiable. In other words, the research hypothesis should create clear predictions than can be tested.
• Ideally, a hypothesis can be drawn from previous theoretical research publications.
• A good research hypothesis is much more than an intelligent guess, and sometimes, a research hypothesis could take the form of research questions that can be explored further via research and suggest an expected result.

Research hypotheses are a vital part of the scientific process that leads to or are the reasons for scientific experiments. That said, a slight flaw in constructing a hypothesis could generate negative results.

There are various types of hypotheses, and the following checklist should guide a good hypothesis.

• Is the language employed clear and direct?
• Is there a good relationship between the hypothesis and the research topic?
• Can the hypothesis be tested?
• What are the methods used to carry out testability?
• What are areas of explanation being addressed?

The essence of this checklist is to get your hypothesis up on the right footing and help you pinpoint any gaps or weaknesses.

The following listed below are the various 7 types of research hypotheses.

1. Simple Hypothesis

It can show the impact of a relationship between a single dependent variable and a single independent variable. For example,

Consuming too many fizzy drinks will cause weight gain and a bloated belly.

2. Complex Hypothesis

It foretells the relationship between multiple independent and dependent variables.

For instance, eating more vegetables and a low-calorie diet would lead to weight loss.

3. Directional Hypothesis

It shows the expected direction required to determine the relationship between variables and is derived from theory. Furthermore, it shows a researcher’s intellectual commitment to a particular outcome by the length of the study.

For example,

Toddlers under the age of 4 who were given well-balanced meals for 5 years showed a higher IQ level than their counterparts who did not have the same treatments.

4. Non-directional Hypothesis

It does not predict the direction or nature of the relationship between the two variables. A non-directional hypothesis is used mostly when there is no theory involved. For example, men and women differ in terms of helpfulness.

5. Associative and Causal Hypothesis

The associative hypothesis shows the interdependency between related variables. A change in one variable results will cause a change in the other variable. However, the change is not caused by either of the variables.

For example, the increase in the number of unhealthy people visiting a particular hospital is not because the hospital is the source of their illness. Rather it could be a result of other unrelated factors like the weather, personal hygiene practices, etc.

On the other hand, the causal hypothesis predicts the effect a change in variables would have on different variables. For instance, a change in the writing style on their blog led to higher user engagement.

6. Null Hypothesis

This refers to a lack of relationship between different variables. For example, plants would grow irrespective of the source of water, natural or artificial. It proposes a negative statement to support the researcher’s discovery, showing that no relationship exists between the two variables.

7. Alternative Hypothesis

The alternative hypothesis is a statement used in statistical experiments. It is the opposite of the null hypothesis and is described by the term H1 or Ha. The term alternative is used because it is the alternative to the null hypothesis. Therefore it is safe to say that it is an alternative theory to the one a researcher is testing and trying to prove.

The Alternative Hypothesis is classified into two categories;

Directional and Non-Directional.

• Directional: A statement outlining the ways the expected outcomes would be collated. It is mostly used in cases where there is a need to establish a relationship between two different things or when comparing various groups. For example, Attending physiotherapy sessions will improve the stage performance of ballerinas.
• Non-directional: This implies no direction for the expected results. For example, attending physiotherapy sessions impacts the stage performance of ballerinas.

The directional statement clearly states that the physiotherapy sessions would boost performance in both examples outlined above. At the same time, the non-directional only acknowledges that the sessions would influence performance without stating whether the influence would be positive or negative.

8. Empirical Hypothesis

When a theory is proven through an experiment and observation, this justifies or validates a claim and distinguishes it from a wild guess.

Here are a few examples that depict the empirical hypothesis:

a. Women who take folate supplements face a lesser risk of having children with congenital disabilities.

b. Good behavior in children can be reinforced when they are rewarded for good behavior.

9. Statistical Hypothesis

It is a statement that postulates a theory based on studying a sample population. It is a logic-based analysis where a specific population is researched to gather evidence to prove a particular theory.

For example:

43% of the American population in the age group of 22-29 speak a second language.

Importance of a Testable Hypothesis

Testability in the hypothesis is crucial in establishing any scientific research in the physical world. This is because research or any science founded on a hypothesis is usually laced with inherent flaws. One of the flaws is the idea that any hypothesis by design significantly reduces the area of exploration, which births experimental results that would fail in real-life scenarios.

This problem is further compounded by modern science, which equates philosophical concepts to physical science. Testability solves these problems by making the research hypothesis more truthful, based on real tenable results. Hence any well-thought-out hypothesis would be founded in testability.

The condition for any viable hypothesis is testability. To be considered testable, the following criteria must be fulfilled.

• There must exist a viable means to prove that the hypothesis is true.
• Similarly, there must be a possibility to prove the hypothesis false.
• Finally, the result of the hypothesis must be replicable.

Without these testability criteria, the hypothesis and proposed results would be indefinite, and the significance of the experiment would be lost.

How To Formulate an Effective Research Hypothesis

There are clear and precise steps to creating an effective research hypothesis. An effective research hypothesis must answer these 6 questions;

What, who, where, when, how, and why?.

In the scientific method, the first step is to ask a question. Frame this question using the classic six highlighted above. For example:

• How long does it take avocados to grow?
• Why do we have shorter days and longer nights in winter?
• What happened to the groundnut pyramids?
• How does a caterpillar become a butterfly?
• Why are students excited on Friday afternoon?
• How does sleep affect motivation?
• Why do tax systems help build an economy?

So the first step is to identify and state what problem you are trying to solve. The hypothesis must clearly define the subject, the experiment’s focus, and the expected outcome.

Put together preliminary research data from a wide range of sources, including academic journals, personal experiments, and observations from the work of others. Afterward, define the variables, and separate the dependent variables from the independent variables.

The independent variables are the ones that are malleable and can be tweaked, controlled, changed, and affected by various conditions. Secondly, independent variables are isolated from other factors of the research.

On the other hand, dependent variables rely on other aspects of the research and are affected by any change in the independent variable.

Refine your hypothesis by emulating the following as a checklist:

• Specific language devoid of any ambiguity must be used.
• Clearly predict the relationship between the variables and the expected outcome.
• No prior assumptions should be made about the reader’s knowledge.
• The results must be testable, relevant, and specific to the research questions.

However, one of the proven methods of determining the effectiveness of your research hypothesis is to compare it to an already-existing hypothesis. It would help guide and make the process easier.

Here are a few general examples that can guide you in formulating your hypothesis:

a. Eating a generous amount of fiber-rich fruits like apples after age 50 would keep the doctor away or limit visits to the doctor’s office.

b. Cheap airlines, referred to as budget airlines, will receive more customer complaints than regular or premium airlines because of the limited amenities provided compared to full-service airlines.

Stating the obvious, the final step is to write your hypothesis using all the steps outlined. It is important to remember that your hypothesis is a statement that shows who or what is being studied, the variables, and your predicted outcome.

Hypothesis in Research

We have already established that a hypothesis is an idea or a statement based on tangible evidence that can be proven. A hypothesis in research is simply a statement concerning the predicted outcome of a scientific study. In this instance, it has to be specific, testable, and falsifiable.

Specific here refers to clarity about the parties involved and the expected results.

For example, a patient’s perception of a doctor’s experience breeds a higher level of trust in the doctor’s diagnosis.

This example depicts the clarity and directness of the subject. There is no ambiguity in the expectations of the relationship referred to.

Testability in research hypothesis is simply saying that the hypothesis must be provable. This means that the data gathered must be collected and observed in a thorough scientific process to assess the quality of the hypothesis. In other words, there must be a proven way to validate the claims of the hypothesis.

For example, the doctor referred to in the previous hypothesis can be validated by other patients’ perceptions of his competence and previous results from past diagnoses. A quantitative research approach using a large number of people would have been used to test the claims of this hypothesis.

The falsifiability in the research hypothesis means that the hypothesis can be refuted. This step is essential in validating or establishing the viability of the hypothesis. Hence there has to be an emphatic way of confirming if a hypothesis is false.

The claim is that life exists on planets like the earth. This claim cannot be a hypothesis because the only way to verify this would be to visit all planets in the world and come back with evidence of life. This claim is not disprovable.

So when conducting a hypothesis in research, it is vital to meet all these criteria to have an effective hypothesis.

Hypothesis in Statistics

A hypothesis in statistics is a legal claim about a subject within the framework of a statistical model. It is a process of statistical inference to determine if the data collated is inadequate to prove a hypothesis. The data used here can be gleaned from a large population. A statistical analyst verifies a hypothesis by analyzing a random sample of the population.

In this case, the random population sample is used to test 2 different hypotheses; the null hypothesis and the alternative hypothesis.

4 Steps of Statistical Hypothesis Testing

There is a four-step process used for statistical hypothesis testing.

• State only two hypotheses; that way, only one can be right.
• Create an analysis plan that shows how the data would be evaluated.
• Implement the plan by physically analyzing the sample data
• Analyze the result and either accept the hypothesis or state the plausible hypothesis based on the given data.

For example, if you want to carry a test on, say, 50% of exceptional college students come from wealthy homes.

The null hypothesis would be that 50%  of the students are from wealthy homes, while the alternative hypothesis would be that 50% of the students are not from wealthy homes.

A random sample of 100 students in the said college would be carried out via a survey, and the null hypothesis would be tested.

If 40 of those students are not from wealthy homes, then the 50% null hypothesis would be rejected, and the alternative hypothesis would be accepted.

Scientific Hypothesis

In the scientific hypothesis, the researcher’s expectation from the experiment is achieved following a scientific method outlined below:

• Create the question
• Carry out a background research
• Creating a hypothesis
• Design an experiment
• Collect data
• Analyze the results
• Reach a conclusion
• Share the results

In the scientific hypothesis, the statement is a prediction; then, it evolves into a question, answered via research. It is at the point the hypothesis states the desired expectation. The next step after this is to test the hypothesis.

For example, the effect of Vitamin C supplements for a patient with cold symptoms is that the medication would help alleviate the effects of the cold.

As we established, a hypothesis predicts a relationship between variables that is yet to be proven. Creating a viable research hypothesis involves conducting research and broadening your knowledge about the subject via studying in other to choose the area of focus. Different types of hypotheses can be adopted to validate your predictions. The hypothesis should be testable in other to validate the claims.

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13 Different Types of Hypothesis

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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There are 13 different types of hypothesis. These include simple, complex, null, alternative, composite, directional, non-directional, logical, empirical, statistical, associative, exact, and inexact.

A hypothesis can be categorized into one or more of these types. However, some are mutually exclusive and opposites. Simple and complex hypotheses are mutually exclusive, as are direction and non-direction, and null and alternative hypotheses.

Below I explain each hypothesis in simple terms for absolute beginners. These definitions may be too simple for some, but they’re designed to be clear introductions to the terms to help people wrap their heads around the concepts early on in their education about research methods .

Types of Hypothesis

Before you Proceed: Dependent vs Independent Variables

A research study and its hypotheses generally examine the relationships between independent and dependent variables – so you need to know these two concepts:

• The independent variable is the variable that is causing a change.
• The dependent variable is the variable the is affected by the change. This is the variable being tested.

Read my full article on dependent vs independent variables for more examples.

Example: Eating carrots (independent variable) improves eyesight (dependent variable).

1. Simple Hypothesis

A simple hypothesis is a hypothesis that predicts a correlation between two test variables: an independent and a dependent variable.

This is the easiest and most straightforward type of hypothesis. You simply need to state an expected correlation between the dependant variable and the independent variable.

You do not need to predict causation (see: directional hypothesis). All you would need to do is prove that the two variables are linked.

Simple Hypothesis Examples

2. Complex Hypothesis

A complex hypothesis is a hypothesis that contains multiple variables, making the hypothesis more specific but also harder to prove.

You can have multiple independent and dependant variables in this hypothesis.

Complex Hypothesis Example

In the above example, we have multiple independent and dependent variables:

• Independent variables: Age and weight.
• Dependent variables: diabetes and heart disease.

Because there are multiple variables, this study is a lot more complex than a simple hypothesis. It quickly gets much more difficult to prove these hypotheses. This is why undergraduate and first-time researchers are usually encouraged to use simple hypotheses.

3. Null Hypothesis

A null hypothesis will predict that there will be no significant relationship between the two test variables.

For example, you can say that “The study will show that there is no correlation between marriage and happiness.”

A good way to think about a null hypothesis is to think of it in the same way as “innocent until proven guilty”[1]. Unless you can come up with evidence otherwise, your null hypothesis will stand.

A null hypothesis may also highlight that a correlation will be inconclusive . This means that you can predict that the study will not be able to confirm your results one way or the other. For example, you can say “It is predicted that the study will be unable to confirm a correlation between the two variables due to foreseeable interference by a third variable .”

Beware that an inconclusive null hypothesis may be questioned by your teacher. Why would you conduct a test that you predict will not provide a clear result? Perhaps you should take a closer look at your methodology and re-examine it. Nevertheless, inconclusive null hypotheses can sometimes have merit.

Null Hypothesis Examples

4. Alternative Hypothesis

An alternative hypothesis is a hypothesis that is anything other than the null hypothesis. It will disprove the null hypothesis.

We use the symbol H A or H 1 to denote an alternative hypothesis.

The null and alternative hypotheses are usually used together. We will say the null hypothesis is the case where a relationship between two variables is non-existent. The alternative hypothesis is the case where there is a relationship between those two variables.

The following statement is always true: H 0 ≠ H A .

Let’s take the example of the hypothesis: “Does eating oatmeal before an exam impact test scores?”

We can have two hypotheses here:

• Null hypothesis (H 0 ): “Eating oatmeal before an exam does not impact test scores.”
• Alternative hypothesis (H A ): “Eating oatmeal before an exam does impact test scores.”

For the alternative hypothesis to be true, all we have to do is disprove the null hypothesis for the alternative hypothesis to be true. We do not need an exact prediction of how much oatmeal will impact the test scores or even if the impact is positive or negative. So long as the null hypothesis is proven to be false, then the alternative hypothesis is proven to be true.

5. Composite Hypothesis

A composite hypothesis is a hypothesis that does not predict the exact parameters, distribution, or range of the dependent variable.

Often, we would predict an exact outcome. For example: “23 year old men are on average 189cm tall.” Here, we are giving an exact parameter. So, the hypothesis is not composite.

But, often, we cannot exactly hypothesize something. We assume that something will happen, but we’re not exactly sure what. In these cases, we might say: “23 year old men are not on average 189cm tall.”

We haven’t set a distribution range or exact parameters of the average height of 23 year old men. So, we’ve introduced a composite hypothesis as opposed to an exact hypothesis.

Generally, an alternative hypothesis (discussed above) is composite because it is defined as anything except the null hypothesis. This ‘anything except’ does not define parameters or distribution, and therefore it’s an example of a composite hypothesis.

6. Directional Hypothesis

A directional hypothesis makes a prediction about the positivity or negativity of the effect of an intervention prior to the test being conducted.

Instead of being agnostic about whether the effect will be positive or negative, it nominates the effect’s directionality.

We often call this a one-tailed hypothesis (in contrast to a two-tailed or non-directional hypothesis) because, looking at a distribution graph, we’re hypothesizing that the results will lean toward one particular tail on the graph – either the positive or negative.

Directional Hypothesis Examples

7. Non-Directional Hypothesis

A non-directional hypothesis does not specify the predicted direction (e.g. positivity or negativity) of the effect of the independent variable on the dependent variable.

These hypotheses predict an effect, but stop short of saying what that effect will be.

A non-directional hypothesis is similar to composite and alternative hypotheses. All three types of hypothesis tend to make predictions without defining a direction. In a composite hypothesis, a specific prediction is not made (although a general direction may be indicated, so the overlap is not complete). For an alternative hypothesis, you often predict that the even will be anything but the null hypothesis, which means it could be more or less than H 0 (or in other words, non-directional).

Let’s turn the above directional hypotheses into non-directional hypotheses.

Non-Directional Hypothesis Examples

8. Logical Hypothesis

A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions.

These are most commonly used in philosophy because philosophical questions are often untestable and therefore we must rely on our logic to formulate logical theories.

Usually, we would want to turn a logical hypothesis into an empirical one through testing if we got the chance. Unfortunately, we don’t always have this opportunity because the test is too complex, expensive, or simply unrealistic.

Here are some examples:

• Before the 1980s, it was hypothesized that the Titanic came to its resting place at 41° N and 49° W, based on the time the ship sank and the ship’s presumed path across the Atlantic Ocean. However, due to the depth of the ocean, it was impossible to test. Thus, the hypothesis was simply a logical hypothesis.
• Dinosaurs closely related to Aligators probably had green scales because Aligators have green scales. However, as they are all extinct, we can only rely on logic and not empirical data.

9. Empirical Hypothesis

An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a ‘working hypothesis’.

We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments. Empirical research relies on tests that can be verified by observation and measurement.

So, an empirical hypothesis is a hypothesis that can and will be tested.

• Raising the wage of restaurant servers increases staff retention.
• Adding 1 lb of corn per day to cows’ diets decreases their lifespan.
• Mushrooms grow faster at 22 degrees Celsius than 27 degrees Celsius.

Each of the above hypotheses can be tested, making them empirical rather than just logical (aka theoretical).

10. Statistical Hypothesis

A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations.

It requires the use of datasets or carefully selected representative samples so that statistical inference can be drawn across a larger dataset.

This type of research is necessary when it is impossible to assess every single possible case. Imagine, for example, if you wanted to determine if men are taller than women. You would be unable to measure the height of every man and woman on the planet. But, by conducting sufficient random samples, you would be able to predict with high probability that the results of your study would remain stable across the whole population.

You would be right in guessing that almost all quantitative research studies conducted in academic settings today involve statistical hypotheses.

Statistical Hypothesis Examples

• Human Sex Ratio. The most famous statistical hypothesis example is that of John Arbuthnot’s sex at birth case study in 1710. Arbuthnot used birth data to determine with high statistical probability that there are more male births than female births. He called this divine providence, and to this day, his findings remain true: more men are born than women.
• Lady Testing Tea. A 1935 study by Ronald Fisher involved testing a woman who believed she could tell whether milk was added before or after water to a cup of tea. Fisher gave her 4 cups in which one randomly had milk placed before the tea. He repeated the test 8 times. The lady was correct each time. Fisher found that she had a 1 in 70 chance of getting all 8 test correct, which is a statistically significant result.

11. Associative Hypothesis

An associative hypothesis predicts that two variables are linked but does not explore whether one variable directly impacts upon the other variable.

We commonly refer to this as “ correlation does not mean causation ”. Just because there are a lot of sick people in a hospital, it doesn’t mean that the hospital made the people sick. There is something going on there that’s causing the issue (sick people are flocking to the hospital).

So, in an associative hypothesis, you note correlation between an independent and dependent variable but do not make a prediction about how the two interact. You stop short of saying one thing causes another thing.

Associative Hypothesis Examples

• Sick people in hospital. You could conduct a study hypothesizing that hospitals have more sick people in them than other institutions in society. However, you don’t hypothesize that the hospitals caused the sickness.
• Lice make you healthy. In the Middle Ages, it was observed that sick people didn’t tend to have lice in their hair. The inaccurate conclusion was that lice was not only a sign of health, but that they made people healthy. In reality, there was an association here, but not causation. The fact was that lice were sensitive to body temperature and fled bodies that had fevers.

12. Causal Hypothesis

A causal hypothesis predicts that two variables are not only associated, but that changes in one variable will cause changes in another.

A causal hypothesis is harder to prove than an associative hypothesis because the cause needs to be definitively proven. This will often require repeating tests in controlled environments with the researchers making manipulations to the independent variable, or the use of control groups and placebo effects .

If we were to take the above example of lice in the hair of sick people, researchers would have to put lice in sick people’s hair and see if it made those people healthier. Researchers would likely observe that the lice would flee the hair, but the sickness would remain, leading to a finding of association but not causation.

Causal Hypothesis Examples

13. Exact vs. Inexact Hypothesis

For brevity’s sake, I have paired these two hypotheses into the one point. The reality is that we’ve already seen both of these types of hypotheses at play already.

An exact hypothesis (also known as a point hypothesis) specifies a specific prediction whereas an inexact hypothesis assumes a range of possible values without giving an exact outcome. As Helwig [2] argues:

“An “exact” hypothesis specifies the exact value(s) of the parameter(s) of interest, whereas an “inexact” hypothesis specifies a range of possible values for the parameter(s) of interest.”

Generally, a null hypothesis is an exact hypothesis whereas alternative, composite, directional, and non-directional hypotheses are all inexact.

See Next: 15 Hypothesis Examples

This is introductory information that is basic and indeed quite simplified for absolute beginners. It’s worth doing further independent research to get deeper knowledge of research methods and how to conduct an effective research study. And if you’re in education studies, don’t miss out on my list of the best education studies dissertation ideas .

[1] https://jnnp.bmj.com/content/91/6/571.abstract

[2] http://users.stat.umn.edu/~helwig/notes/SignificanceTesting.pdf

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Number Games for Kids (Free and Easy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Word Games for Kids (Free and Easy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Outdoor Games for Kids
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 50 Incentives to Give to Students

2 thoughts on “13 Different Types of Hypothesis”

Wow! This introductionary materials are very helpful. I teach the begginers in research for the first time in my career. The given tips and materials are very helpful. Chris, thank you so much! Excellent materials!

You’re more than welcome! If you want a pdf version of this article to provide for your students to use as a weekly reading on in-class discussion prompt for seminars, just drop me an email in the Contact form and I’ll get one sent out to you.

When I’ve taught this seminar, I’ve put my students into groups, cut these definitions into strips, and handed them out to the groups. Then I get them to try to come up with hypotheses that fit into each ‘type’. You can either just rotate hypothesis types so they get a chance at creating a hypothesis of each type, or get them to “teach” their hypothesis type and examples to the class at the end of the seminar.

Cheers, Chris

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Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions
• Testing ideas with evidence from the natural world is at the core of science.
• Scientific testing involves figuring out what we would  expect  to observe if an idea were correct and comparing that expectation to what we  actually  observe.
• Scientific arguments are built from an idea and the evidence relevant to that idea.
• Scientific arguments can be built in any order. Sometimes a scientific idea precedes any evidence relevant to it, and other times the evidence helps inspire the idea.

Misconception:  Science proves ideas.

Misconception:  Science can only disprove ideas.

Correction:  Science neither proves nor disproves. It accepts or rejects ideas based on supporting and refuting evidence, but may revise those conclusions if warranted by new evidence or perspectives.  Read more about it.

The core of science: Relating evidence and ideas

In this case, the term  argument  refers not to a disagreement between two people, but to an evidence-based line of reasoning — so scientific arguments are more like the closing argument in a court case (a logical description of what we think and why we think it) than they are like the fights you may have had with siblings. Scientific arguments involve three components: the idea (a  hypothesis  or theory), the  expectations  generated by that idea (frequently called predictions), and the actual observations relevant to those expectations (the evidence). These components are always related in the same logical way:

• What would we expect to see if this idea were true (i.e., what is our expected observation)?
• What do we actually observe?
• Do our expectations match our observations?

PREDICTIONS OR EXPECTATIONS?

When scientists describe their arguments, they frequently talk about their expectations in terms of what a hypothesis or theory predicts: “If it were the case that smoking causes lung cancer, then we’d  predict  that countries with higher rates of smoking would have higher rates of lung cancer.” At first, it might seem confusing to talk about a prediction that doesn’t deal with the future, but that refers to something going on right now or that may have already happened. In fact, this is just another way of discussing the expectations that the hypothesis or theory generates. So when a scientist talks about the  predicted  rates of lung cancer, he or she really means something like “the rates that we’d expect to see if our hypothesis were correct.”

If the idea generates expectations that hold true (are actually observed), then the idea is more likely to be accurate. If the idea generates expectations that don’t hold true (are not observed), then we are less likely to  accept  the idea. For example, consider the idea that cells are the building blocks of life. If that idea were true, we’d expect to see cells in all kinds of living tissues observed under a microscope — that’s our expected observation. In fact, we do observe this (our actual observation), so evidence supports the idea that living things are built from cells.

Though the structure of this argument is consistent (hypothesis, then expectation, then actual observation), its pieces may be assembled in different orders. For example, the first observations of cells were made in the 1600s, but cell theory was not postulated until 200 years later — so in this case, the evidence actually helped inspire the idea. Whether the idea comes first or the evidence comes first, the logic relating them remains the same.

Here, we’ll explore scientific arguments and how to build them. You can investigate:

Putting the pieces together: The hard work of building arguments

• Predicting the past
• Arguments with legs to stand on

Or just click the  Next  button to dive right in!

• Take a sidetrip
• Teaching resources

Scientific arguments rely on testable ideas. To learn what makes an idea testable, review our  Science Checklist .

• Forming hypotheses — scientific explanations — can be difficult for students. It is often easier for students to generate an expectation (what they think will happen or what they expect to observe) based on prior experience than to formulate a potential explanation for that phenomena. You can help students go beyond expectations to generate real, explanatory hypotheses by providing sentence stems for them to fill in: “I expect to observe A because B.” Once students have filled in this sentence you can explain that B is a hypothesis and A is the expectation generated by that hypothesis.
• You can help students learn to distinguish between hypotheses and the expectations generated by them by regularly asking students to analyze lecture material, text, or video. Students should try to figure out which aspects of the content were hypotheses and which were expectations.

Summing up the process

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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach

• Open access
• Published: 12 August 2024
• Volume 57 , article number  244 , ( 2024 )

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• Uchenna Akujuobi 1   na1 ,
• Priyadarshini Kumari 2   na1 ,
• Jihun Choi 3 ,
• Samy Badreddine 1 ,
• Kana Maruyama 3 ,
• Sucheendra K. Palaniappan 4 &
• Tarek R. Besold 1  

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Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously implicit connections between scientific concepts/entities within often extensive bodies of literature explicit—i.e., suggesting novel scientific hypotheses. In doing so, understanding the temporally evolving interactions between these entities can provide valuable information for predicting the future development of entity relationships. However, existing methods often underutilize the latent information embedded in the temporal aspects of the interaction data. Motivated by applications in the food domain—where we aim to connect nutritional information with health-related benefits—we address the hypothesis-generation problem using a temporal graph-based approach. Given that hypothesis generation involves predicting future (i.e., still to be discovered) entity connections, in our view the ability to capture the dynamic evolution of connections over time is pivotal for a robust model. To address this, we introduce THiGER , a novel batch contrastive temporal node-pair embedding method. THiGER excels in providing a more expressive node-pair encoding by effectively harnessing node-pair relationships. Furthermore, we present THiGER-A , an incremental training approach that incorporates an active curriculum learning strategy to mitigate label bias arising from unobserved connections. By progressively training on increasingly challenging and high-utility samples, our approach significantly enhances the performance of the embedding model. Empirical validation of our proposed method demonstrates its effectiveness on established temporal-graph benchmark datasets, as well as on real-world datasets within the food domain.

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1 Introduction

The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom. — Isaac Asimov. Science is advancing at an increasingly quick pace, as evidenced, for instance, by the exponential growth in the number of published research articles per year (White 2021 ). Effectively navigating this ever-growing body of knowledge is tedious and time-consuming in the best of cases, and more often than not becomes infeasible for individual scientists (Brainard 2020 ). In order to augment the efforts of human scientists in the research process, computational approaches have been introduced to automatically extract hypotheses from the knowledge contained in published resources. Swanson ( 1986 ) systematically used a scientific literature database to find potential connections between previously disjoint bodies of research, as a result hypothesizing a (later confirmed) curative relationship between dietary fish oils and Raynaud’s syndrome . Swanson and Smalheiser then automatized the search and linking process in the ARROWSMITH system (Swanson and Smalheiser 1997 ). Their work and other more recent examples (Fan and Lussier 2017 ; Trautman 2022 ) clearly demonstrate the usefulness of computational methods in extracting latent information from the vast body of scientific publications.

Over time, various methodologies have been proposed to address the Hypothesis Generation (HG) problem. Swanson and Smalheiser (Smalheiser and Swanson 1998 ; Swanson and Smalheiser 1997 ) pioneered the use of a basic ABC model grounded in a stringent interpretation of structural balance theory (Cartwright and Harary 1956 ). In essence, if entities A and B, as well as entities A and C, share connections, then entities B and C should be associated. Subsequent years have seen the exploration of more sophisticated machine learning-based approaches for improved inference. These encompass techniques such as text mining (Spangler et al. 2014 ; Spangler 2015 ), topic modeling (Sybrandt et al. 2017 ; Srihari et al. 2007 ; Baek et al. 2017 ), association rules (Hristovski et al. 2006 ; Gopalakrishnan et al. 2016 ; Weissenborn et al. 2015 ), and others (Jha et al. 2019 ; Xun et al. 2017 ; Shi et al. 2015 ; Sybrandt et al. 2020 )

In the context of HG, where the goal is to predict novel relationships between entities extracted from scientific publications, comprehending prior relationships is of paramount importance. For instance, in the domain of social networks, the principles of social theory come into play when assessing the dynamics of connections between individuals. When there is a gradual reduction in the social distance between two distinct individuals, as evidenced by factors such as the establishment of new connections with shared acquaintances and increased geographic proximity, there emerges a heightened likelihood of a subsequent connection between these two individuals (Zhang and Pang 2015 ; Gitmez and Zárate 2022 ). This concept extends beyond social networks and finds relevance in predicting scientific relationships or events through the utilization of temporal information (Crichton et al. 2018 ; Krenn et al. 2023 ; Zhang et al. 2022 ). In both contexts, the principles of proximity and evolving relationships serve as valuable indicators, enabling a deeper understanding of the intricate dynamics governing these complex systems.

Modeling these relationships’ temporal evolution assumes a critical role in constructing an effective and resilient hypothesis generation model. To harness the temporal dynamics, Akujuobi et al. ( 2020b , 2020a ) and Zhou et al. ( 2022 ) conceptualize the HG task as a temporal graph problem. More precisely, given a sequence of graphs \(G = \{G_{0}, G_{2},\ldots , G_{T} \}\) , the objective is to deduce which previously unlinked nodes in \(G_{T}\) ought to be connected. In this framework, nodes denote biomedical entities, and the graphs \(G_{\tau }\) represent temporal graphlets (see Fig.  1 ).

Definition 1

Temporal graphlet : A temporal graphlet \(G_{\tau } = \{V^{\tau },E^{\tau }\}\) is a temporal subgraph at time point \(\tau\) , where \(V^{\tau } \subset V\) and \(E^{\tau } \subset E\) are the temporal set of nodes and edges of the subgraph.

Their approach tackles the HG problem by introducing a temporal perspective. Instead of relying solely on the final state \(E_{T}\) on a static graph, it considers how node pairs evolve over discrete time steps \(E^{\tau }: \tau = 0 \dots T\) . To model this sequential evolution effectively, Akujuobi et al. and Zhou et al. leverage the power of recurrent neural networks (RNNs) (see Fig.  2 a). However, it is essential to note that while RNNs have traditionally been the preferred choice for HG, their sequential nature may hinder capturing long-range dependencies, impacting performance for lengthy sequences.

Modeling hypothesis generation as a temporal link prediction problem

Predicting the link probability \(p_{i,j}\) for a node pair \(v_i\) and \(v_j\) using a a Recurrent Neural Network approach (Akujuobi et al. 2020b ; Zhou et al. 2022 ), b THiGER, our approach. The recurrent approach aggregates the neighborhood information \({{\mathcal {N}}}^t(v_i)\) and \({{\mathcal {N}}}^t(v_j)\) sequentially while THiGER aggregates the neighborhood information hierarchically in parallel

To shed these limitations, we propose THiGER ( T emporal Hi erarchical G raph-based E ncoder R epresentation), a robust transformer-based model designed to capture the evolving relationships between node pairs. THiGER overcomes the constraints of previous methods by representing temporal relationships hierarchically (see Fig.  2 b). The proposed hierarchical layer-wise framework presents an incremental approach to comprehensively model the temporal dynamics among given concepts. It achieves this by progressively extracting the temporal interactions between consecutive time steps, thus enabling the model to prioritize attention to the informative regions of temporal evolution during the process. Our method effectively addresses issues arising from imbalanced temporal information (see Sect.  5.2 ). Moreover, it employs a contrastive learning strategy to improve the quality of task-specific node embeddings for node-pair representations and relationship inference tasks.

An equally significant challenge in HG is the lack of negative-class samples for training. Our dataset provides positive-class samples, which represent established connections between entities, but it lacks negative-class samples denoting non-existent connections (as opposed to undiscovered connections, which could potentially lead to scientific breakthroughs). This situation aligns with the positive-unlabeled (PU) learning problem. Prior approaches have typically either discarded unobserved connections as uninformative or wrongly treated them as negative-class samples. The former approach leads to the loss of valuable information, while the latter introduces label bias during training.

In response to these challenges, we furthermore introduce THiGER-A, an active curriculum learning strategy designed to train the model incrementally. THiGER-A utilizes progressively complex positive samples and highly informative, diverse unobserved connections as negative-class samples. Our experimental results demonstrate that by employing incremental training with THiGER-A, we achieve enhanced convergence and performance for hypothesis-generation models compared to training on the entire dataset in one go. Remarkably, our approach demonstrates strong generalization capabilities, especially in challenging inductive test scenarios where the entities were not part of the seen training dataset.

Inspired by Swanson’s pioneering work, we chose the food domain as a promising application area for THiGER. This choice is motivated by the increasing prevalence of diet-related health conditions, such as obesity and type-2 diabetes, alongside the growing recognition and utilization of the health benefits associated with specific food products in wellness and medical contexts.

In summary, our contributions are as follows:

Methodology: We propose a novel temporal hierarchical transformer-based architecture for node pair encoding. In utilizing the temporal batch-contrastive strategy, our architecture differs from existing approaches that learn in conventional static or temporal graphs. In addition, we present a novel incremental training strategy for temporal graph node pair embedding and future relation prediction. This strategy effectively mitigates negative-label bias through active learning and improves generalization by training the model progressively on increasingly complex positive samples using curriculum learning.

Evaluation: We test the model’s efficacy on several real-world graphs of different sizes to give evidence for the model’s strength for temporal graph problems and hypothesis generation. The model is trained end-to-end and shows superior performance on HG tasks.

Application: To the best of our knowledge, this is the first application of temporal hypothesis generation in the health-related food domain. Through case studies, we validate the practical relevance of our findings.

The remaining sections of this paper include a discussion of related work in Sect.  2 , a detailed introduction of the proposed THiGER model and the THiGER-A active curriculum learning strategy in Sect.  3 , an overview of the datasets, the model setup and parameter tuning, and our evaluation approach in Sect. 4 , the results of our experimental evaluations in Sect.  5 , and finally, our conclusions and a discussion of future work in Sect.  6 .

2 Related works

2.1 hypothesis generation.

The development of effective methods for machine-assisted discovery is crucial in pushing scientific research into the next stage (Kitano 2021 ). In recent years, several approaches have been proposed in a bid to augment human abilities relevant to the scientific research process including tools for research design and analysis (Tabachnick and Fidell 2000 ), process modelling and simulation (Klein et al. 2002 ), or scientific hypothesis generation (King et al. 2004 , 2009 ).

The early pioneers of the hypothesis generation domain proposed the so called ABC model for generating novel scientific hypothesis based on existing knowledge (Swanson 1986 ; Swanson and Smalheiser 1997 ). ABC-based models are simple and efficient, and have been implemented in classical hypothesis generation systems such as ARROWSMITH (Swanson and Smalheiser 1997 ). However, several drawbacks remain, including the need for similarity metrics defined on heuristically determined term lists and significant costs in terms of computational complexity with respect to the size of common entities.

More recent approaches, thus, have aimed to curtain the limitation of the ABC model. Spangler et al. ( 2014 ); Spangler ( 2015 ) proposed text mining techniques to identify entity relationships from unstructured medical texts. AGATHA (Sybrandt et al. 2020 ) used a transformer encoder architecture to learn the ranking criteria between regions of a given semantic graph and the plausibility of new research connections. Srihari et al. ( 2007 ); Baek et al. ( 2017 ) proposed several text mining approaches to detect how concepts are linked within and across multiple text documents. Sybrandt et al. ( 2017 ) proposed incorporating machine learning techniques such as clustering and topical phrase mining. Shi et al. ( 2015 ) modeled the probability that concepts will be linked based on a given time window using random walks.

The previously mentioned methods do not consider temporal attributes of the data. More recent works (Jha et al. 2019 ; Akujuobi et al. 2020a ; Zhou et al. 2022 ; Xun et al. 2017 ) argue that capturing the temporal information available in scholarly data can lead to better predictive performance. Jha et al. ( 2019 ) explored the co-evolution of concepts across knowledge bases using a temporal matrix factorization framework. Xun et al. ( 2017 ) modeled concepts’ co-occurrence probability using their temporal embedding. Akujuobi et al. ( 2020a , 2020b ) and Zhou et al. ( 2022 ) captured the temporal information in the scholarly data using RNN techniques.

Our approach captures the dynamic relationship information using a temporal hierarchical transformer encoder model. This strategy alleviates the limitations of the RNN-based models. Furthermore, with the incorporation of active curriculum learning strategies, our model can incrementally learn from the data.

2.2 Temporal graph learning

Learning on temporal graphs has received considerable attention from the research community in recent years. Some works (Hisano 2018 ; Ahmed et al. 2016 ; Milani Fard et al. 2019 ) apply static methods on aggregated graph snapshots. Others, including (Zhou et al. 2018 ; Singer et al. 2019 ), utilize time as a regularizer over consecutive snap-shots of the graph to impose a smoothness constraint on the node embeddings. A popular category of approaches for dynamic graphs is to introduce point processes that are continuous in time. DyRep (Trivedi et al. 2019 ) models the occurrence of an edge as a point process using graph attention on the destination node neighbors. Dynamic-Triad (Zhou et al. 2018 ) models the evolution patterns in a graph by imposing a triadic closure-where a triad with three nodes is developed from an open triad (i.e., with two nodes not connected).

Some recent works on temporal graphs apply several combinations of GNNs and recurrent architectures (e.g., GRU). EvolveGCN (Pareja et al. 2020 ) adapts the graph convolutional network (GCN) model along the temporal dimension by using an RNN to evolve the GCN parameters. T-PAIR (Akujuobi et al. 2020b , a ) recurrently learns a node pair embedding by updating GraphSAGE parameters using gated neural networks (GRU). TGN (Rossi et al. 2020 ) introduces a memory module framework for learning on dynamic graphs. TDE (Zhou et al. 2022 ) captures the local and global changes in the graph structure using hierarchical RNN structures. TNodeEmbed (Singer et al. 2019 ) proposes the use of orthogonal procrustes on consecutive time-step node embeddings along the time dimension.

However, the limitation of RNN remains due to their sequential nature and robustness especially when working on a long timeline. Since the introduction of transformers, there has been interest in their application on temporal graph data. More related to this work, Zhong and Huang ( 2023 ) and Wang et al. ( 2022 ) both propose the use of a transformer architecture to aggregate the node neighborhood information while updating the memory of the nodes using GRU. TLC (Wang et al. 2021a ) design a two-stream encoder that independently processes temporal neighborhoods associated with the two target interaction nodes using a graph-topology-aware Transformer and then integrates them at a semantic level through a co-attentional Transformer.

Our approach utilizes a single hierarchical encoder model to better capture the temporal information in the network while simultaneously updating the node embedding on the task. The model training and node embedding learning is performed end-to-end.

2.3 Active curriculum learning

Active learning (AL) has been well-explored for vision and learning tasks (Settles 2012 ). However, most of the classical techniques rely on single-instance-oracle strategies, wherein, during each training round, a single instance with the highest utility is selected using measures such as uncertainty sampling (Kumari et al. 2020 ), expected gradient length (Ash et al. 2020 ), or query by committee (Gilad-Bachrach et al. 2006 ). The single-instance-oracle approach becomes computationally infeasible with large training datasets such as ours. To address these challenges, several batch-mode active learning methods have been proposed (Priyadarshini et al. 2021 ; Kirsch et al. 2019 ; Pinsler et al. 2019 ). Priyadarshini et al. ( 2021 ) propose a method for batch active metric learning, which enables sampling of informative and diverse triplet data for relative similarity ordering tasks. In order to prevent the selection of correlated samples in a batch, Kirsch et al. ( 2019 ); Pinsler et al. ( 2019 ) develop distinct methods that integrate mutual information into the utility function. All three approaches demonstrate effectiveness in sampling diverse batches of informative samples for metric learning and classification tasks. However, none of these approaches can be readily extended to our specific task of hypothesis prediction on an entity-relationship graph.

Inspired by human learning, Bengio et al. ( 2009 ) introduced the concept of progressive training, wherein the model is trained on increasingly difficult training samples. Various prior works have proposed different measures to quantify the difficulty of training examples. Hacohen and Weinshall ( 2019 ) introduced curriculum learning by transfer, where they developed a score function based on the prediction confidence of a pre-trained model. Wang et al. ( 2021b ) proposed a curriculum learning approach specifically for graph classification tasks. Another interesting work is relational curriculum learning (RCL) (Zhang et al. 2023 ) suggests training the model progressively on complex samples. Unlike most prior work, which typically consider data to be independent, RCL quantifies the difficulty level of an edge by aggregating the embeddings of the neighboring nodes. While their approach utilizes similar relational data to ours, their method does not specifically tackle the challenges inherent to the PU learning setting, which involves sampling both edges and unobserved relationships from the training data. In contrast, our proposed method introduces an incremental training strategy that progressively trains the model by focusing on positive edges of increasing difficulty, as well as incorporating highly informative and diverse negative edges.

Schematic representation of the proposed model for temporal node-pair link prediction. In a , the hierarchical graph transformer model takes as input the aggregated node pair embeddings obtained at each time step \(\tau\) , these temporal node pair embeddings are further encoded and aggregated at each encoder layer. The final output is the generalized node pair embedding across all time steps. In b , a general overview of the model is given, highlighting the incorporation of the Active Curriculum Learning strategy

3 Methodology

3.1 notation.

\(G = \{G_0, \dots , G_T\}\) is a temporal graph such that \(G_\tau = \{V^\tau , E^\tau \}\) evolves over time \(\tau =0\dots T\) ,

\(e(v_i, v_j)\) or \(e_{ij}\) is used to denote the edge between nodes \(v_i\) and \(v_j\) , and \((v_i, v_j)\) is used to denote the node pair corresponding to the edge,

\(y_{i,j}\) is the label associated with the edge \(e(v_i,v_j)\) ,

\({{\mathcal {N}}}^{{\tau }}(v_{})\) gives the neighborhood of a node v in \(V^\tau\) ,

\(x_{v_{}}\) is the embedding of a node v and is static across time steps,

\(z_{i,j}^{\tau }\) is the embedding of a node pair \(\langle v_i, v_j \rangle\) . It depends on the neighborhood of the nodes at a time step \(\tau\) ,

\(h_{i,j}^{[\tau _0,\tau _f]}\) is the embedding of a node pair over a time step window \(\tau _0, \dots , \tau _f\) where \(0 \le \tau _0 \le \tau _f \le T\) ,

\(f(.; \theta )\) is a neural network depending on a set of parameters \(\theta\) . For brevity, \(\theta\) can be omitted if it is clear from the context.

\(E^+\) and \(E^-\) are the subsets of positive and negative edges, denoting observed and non-observed connections between biomedical concepts, respectively.

L is the number of encoder layers in the proposed model.

Hierarchical Node-Pair Embedding \(h_{i,j}^{[\tau _0,\tau _f]}\)

Link Prediction

3.2 Model overview

The whole THiGER(-A) model is shown in Fig.  3 b. Let \(v_i, v_j \in V_T\) be nodes denoting two concepts. The pair is assigned a positive label \(y_{i,j} = 1\) if a corresponding edge (i.e., a link) is observed in \(G_T\) . That is, \(y_{i,j} = 1\) iff \(e(v_i, v_j) \in E^{T}\) , otherwise 0. The model predicts a score \(p_{i,j}\) that reflects \(y_{i,j}\) . The prediction procedure is presented in Algorithm 2.

The link prediction score is given by a neural classifier \(p_{i,j} = f_C(h_{i,j}^{[0,T]}; \theta _C)\) , where \(h_{i,j}^{[0,T]}\) is an embedding vector for the node pair. This embedding is calculated in Algorithm 1 using a hierarchical transformer encoder and illustrated in Fig.  3 a.

The input to the hierarchical encoder layer is the independent local node pair embedding aggregation at each time step shown in line 3 of algorithm 1 as

where \({\textbf{x}}_{{{\mathcal {N}}}^{{\tau }}(v_{i})} = \{x_{v'}: v' \in {{\mathcal {N}}}^{{\tau }}(v_{i})\}\) and \({\textbf{x}}_{{{\mathcal {N}}}^{{\tau }}(v_{j})} = \{x_{v'}: v' \in {{\mathcal {N}}}^{{\tau }}(v_{j})\}\) are the embeddings of the neighbors of \(x_{v_{i}}\) and \(x_{v_{j}}\) at the given time step.

Subsequently, the local node pair embeddings aggregation is processed by the aggregation layer illustrated in Fig.  3 a and shown in line 10 of Algorithm 1. At each hierarchical layer, temporal node pair embeddings are calculated for a sub-window using

where n represents the sub-window size. When necessary, we ensure an even number of leaves to aggregate by adding zero padding values \(H_\textrm{padding} = {\textbf{0}}_d\) , where d is the dimension of the leaf embeddings. The entire encoder architecture is denoted as \(f_E = { f^l_E: l=1\dots L}\) .

In this work, the classifier \(f_C(.; \theta _C)\) is modeled using a multilayer perceptron network (MLP), \(f_A(.; \theta _A)\) is elaborated in Sect.  3.3 , and \(f_E(.;\theta _E)\) is modeled by a multilayer transformer encoder network, which is detailed in Sect.  3.4 .

3.3 Neighborhood aggregation

The neighborhood aggregation is modeled using GraphSAGE (Hamilton et al. 2017 ). GraphSAGE uses K layers to iteratively aggregate a node embedding \(x_{v_{}}\) and its neighbor embeddings \({\textbf{x}}_{{{\mathcal {N}}}^{{\tau }}(v_{})} = \{x_{v'}, v' \in {{\mathcal {N}}}^{{\tau }}(v_{})\}\) . \(f_A\) uses the GraphSAGE block to aggregate \((x_{v_{i}}, {\textbf{x}}_{{{\mathcal {N}}}^{{\tau }}(v_{i})})\) and \((x_{v_{j}}, {\textbf{x}}_{{{\mathcal {N}}}^{{\tau }}(v_{j})})\) in parallel, then merges the two aggregated representations using a MLP layer. In this paper, we explore three models based on the aggregation technique used at each iterative step of GraphSAGE.

Mean Aggregation: This straightforward technique amalgamates neighborhood representations by computing element-wise means of each node’s neighbors and subsequently propagating this information iteratively. For all nodes within the specified set:

Here, \(\beta _{v}^{k}\) denotes the aggregated vector at iteration k , and \(\beta ^{k-1}_{v}\) at iteration \(k-1\) . \(W^S\) and \(W^N\) represent trainable weights, and \(\sigma\) constitutes a sigmoid activation, collectively forming a conventional MLP layer.

GIN (Graph Isomorphism Networks): Arguing that traditional graph aggregation methods, like mean aggregation, possess limited expressive power, GIN introduces the concept of aggregating neighborhood representations as follows:

In this formulation, \(\epsilon ^{k}\) governs the relative importance of the node compared to its neighbors at layer k and can be a learnable parameter or a fixed scalar.

Multi-head Attention: We introduce a multi-head attention-based aggregation technique. This method aggregates neighborhood representations by applying multi-head attention to the node and its neighbors at each iteration:

Here, \(\phi\) represents a multi-head attention function, as detailed in Vaswani et al. ( 2017 ).

3.3.1 Neighborhood definition

To balance performance and scalability considerations, we adopt the neighborhood sampling approach utilized in GraphSAGE to maintain a consistent computational footprint for each batch of neighbors. In this context, we employ a uniform sampling method to select a neighborhood node set of fixed size, denoted as \({{\mathcal {N}}}^{'}(v_{}) \subset {{\mathcal {N}}}^{{\tau }}(v_{})\) , from the original neighbor set at each step. This sampling procedure is essential as, without it, the memory and runtime complexity of a single batch becomes unpredictable and, in the worst-case scenario, reaches a prohibitive \({{\mathcal {O}}}(|V|)\) , making it impractical for handling large graphs.

3.4 Temporal hierarchical multilayer encoder layer

The temporal hierarchical multilayer encoder is the fundamental component of our proposed model, responsible for processing neighborhood representations collected over multiple time steps, specifically \((z_{i,j}^{0}, z_{i,j}^{1}, \dots , z_{i,j}^{T})\) . These neighborhood representations are utilized to construct a hierarchical tree.

At the initial hierarchical layer, we employ an encoder, denoted as \(f_E^1\) , to distill adjacent sequential local node-pair embeddings, represented as \((z_{i,j}^{\tau }, z_{i,j}^{\tau + 1})\) , combining them into a unified embedding, denoted as \(h_{i,j}^{[\tau ,\tau +1]}\) . In cases where the number of time steps is not an even multiple of 2, a zero-vector dummy input is appended.

This process repeats at each hierarchical level l within the tree, with \(h_{i,j}^{[\tau -n,\tau ]} = f^l_E(h_{i,j}^{[\tau -n,\tau -\frac{n}{2}]}, h_{i,j}^{[(\tau -\frac{n}{2}) + 1,\tau ]};\theta ^l_E)\) . Each layer \(f_E^l\) consists of a transformer encoder block and may contain \(N - 1\) encoder sublayers, where \(N \ge 1\) . This mechanism can be viewed as an iterative knowledge aggregation process, wherein the model progressively summarizes the information from pairs of local node pair embeddings.

The output of each encoder layer, denoted as \(h_{i,j}^{[\tau _0,\tau _f]}\) , offers a comprehensive summary of temporal node pair information from time step \(\tau _0\) to \(\tau _f\) . Finally, the output of the last layer, \(h_{i,j}^{[0,T]}\) , is utilized for inferring node pair relationships.

3.5 Parameter learning

The trainable parts of the architecture are the weights and parameters of the neighborhood aggregator \(f_A\) , the transformer network \(f_E\) , the classifier \(f_C\) and the embedding representations \(\{x_{v_{}}: v \in V\}\) .

To obtain suitable representations, we employ a combination of supervised and contrastive loss functions on the output of the hierarchical encoder layer \(h_{i,j}^{[0,T]}\) . The contrastive loss function encourages the embeddings of positive (i.e. a link exists in \(E^T\) ) node pairs to be closer while ensuring that the embeddings of negative node pairs are distinct.

We adopt a contrastive learning framework (Chen et al. 2020 ) to distinguish between positive and negative classes. For brevity, we temporarily denote \(h_{i,j}^{[0,T]}\) as \(h_{i,j}\) . Given two positive node pairs with corresponding embeddings \(e(v_i, v_j) \rightarrow h_{i,j}\) and \(e(v_o, v_n) \rightarrow h_{o,n}\) , the loss function is defined as follows:

where \(\alpha\) represents a temperature parameter, B is a set of node pairs in a given batch, and \(\mathbbm {1}_{(k,w) \ne (i,j)}\) indicates that the labels of node pair ( k ,  w ) and ( i ,  j ) are different. We employ the angular similarity function \(\textrm{sim}(x)=1 - \arccos (x)/\pi\) . We do not explicitly sample negative examples, following the methodology outlined in Chen et al. ( 2020 ).

The contrastive loss is summed over the positive training data \(E^+\) :

To further improve the discriminative power of the learned features, we also minimize the center loss:

where E is the data of positive and negative edges, \(y_{i,j}\) is the class of the pair (0 or 1), \(c_{y_{{i,j}}} \in R^d\) denotes the corresponding class center. The class centers are updated after each mini-batch step following the method proposed in Wen et al. ( 2016 ).

Finally, a good node pair vector \(h_{i,j}^{[0,T]}\) should minimize the binary cross entropy loss of the node pair prediction task:

We adopt the joint supervision of the prediction loss, contrastive loss, and center loss to jointly train the model for discriminative feature learning and relationship inference:

As is usual, the losses are applied over subsets of the entire dataset. In this case, we have an additional requirement for pairs of nodes in \(E^-\) : at least one of the two nodes needs to appear in \(E^+\) . An elaborate batch sampling strategy is proposed in the following section. The model parameters are trained end to end.

Training Procedure in THiGER-A

3.6 Incremental training strategy

This section introduces the incremental training strategy THiGER-A , which extends our base THiGER model. The pseudo-code for THiGER-A is presented in Algorithm 3. We represent the parameters used in the entire architecture as \(\varvec{\theta }= (\theta _A, \theta _E, \theta _C)\) . Let \(P(y \mid e_{i,j}; \varvec{\theta })\) , where \(y\in \{0,1\}\) , denote the link predictor for the nodes \((v_i, v_j)\) . Specifically, in shorthand, we denote \(P(y=1 \mid e_{i,j};\varvec{\theta })\) by \(p_{i,j}\) as in line 3 of Algorithm 2, likewise \(P(y=0\mid e_{i,j}; \varvec{\theta }) = 1 - p_{i,j}\) .

We define \(E^- = (V \times V) \setminus E\) as the set of negative edges representing non-observed connections in the graph. The size of the negative set grows quadratically with the number of nodes, resulting in a computational complexity of \({{\mathcal {O}}}(|V|^2)\) . For large, sparse graphs like ours, the vast number of negative edges makes it impractical to use all of them for model training.

Randomly sampling negative examples may introduce noise and hinder training convergence. To address this challenge, we propose an approach to sample a smaller subset of “informative” negative edges that effectively capture the entity relationships within the graph. Leveraging active learning, a technique for selecting high-utility datasets, we aim to choose a subset \(B^*_N \subset E^-\) that leads to improved model learning.

3.6.1 Negative Edge Sampling using Active Learning

Active learning (AL) is an iterative process centered around acquiring a high-utility subset of samples and subsequently retraining the model. The initial step involves selecting a subset of samples with high utility, determined by a specified informativeness measure. Once this subset is identified, it is incorporated into the training data, and the model is subsequently retrained. This iterative cycle, involving sample acquisition and model retraining, aims to improve the model’s performance and generalizability through the learning process.

In this context, we evaluate the informativeness of edges using a score function denoted as \(S_{AL}: (v_{i}^{-}, v_{j}^{-}) \rightarrow {\mathbb {R}}\) . An edge \((v_{i}^{-}, v_{j}^{-})\) is considered more informative than \((v_{k}^{-}, v_{l}^{-})\) if \(S_{AL}(v_{i}^{-}, v_{j}^{-}) > S_{AL}(v_{k}^{-}, v_{l}^{-})\) . The key challenge in AL lies in defining \(S_{AL}\) , which encodes the learning of the model \(P(.;\varvec{\theta })\) trained in the previous iteration.

We gauge the informativeness of an edge based on model uncertainty. An edge is deemed informative when the current model \(P(.;\varvec{\theta })\) exhibits high uncertainty in predicting its label. Uncertainty sampling is one of the most popular choices for the quantification of informativeness due to its simplicity and high effectiveness in selecting samples for which the model lacks sufficient knowledge. Similar to various previous techniques, we use Shannon entropy to approximate informativeness (Priyadarshini et al. 2021 ; Kirsch et al. 2019 ). It is important to emphasize that ground truth labels are unavailable for negative edges, which represent unobserved entity connections. Therefore, to estimate the informativeness of negative edges, we calculate the expected Shannon entropy across all possible labels. Consequently, the expected entropy for a negative edge \((v_{i}^{-}, v_{j}^{-})\) at the \(m^{th}\) training round is defined as:

Here, \(\varvec{\theta }^{m-1}\) is the base hypothesis predictor model trained at the \((m-1)^{th}\) training round and \(m = 0, 1, \cdots , M\) denotes the AL training round. Selecting a subset of uncertain edges, \(B_{U}\) using Eq.  12 unfortunately does not ensure diversity among the selected subset. The diversity metric is crucial in subset selection as it encourages the selection of diverse samples within the embedding space. This, in turn, results in a higher cumulative informativeness for the selected subset, particularly when the edges exhibit overlapping features. The presence of a highly-correlated edges in the selected subset can lead to a sub-optimal batch with high redundancy. The importance of diversity in selecting informative edges has been emphasized in several prior works (Kirsch et al. 2019 ; Priyadarshini et al. 2021 ). To obtain a diverse subset, both approaches aim to maximize the joint entropy (and consequently, minimize mutual information) among the samples in the selected batch. However, maximizing joint entropy is an expensive combinatorial optimization problem and does not scale well for larger datasets, as in our case.

We adopt a similar approach as (Kumari et al. 2020 ) and utilize the k-means++ algorithm (Arthur and Vassilvitskii 2006 ) to cluster the selected batch \(B_U\) into diverse landmark points. While (Kumari et al. 2020 ) is tailored for metric learning tasks with the triplet samples as inputs, our adaptation of the k-means++ algorithm is designed for graph datasets, leading to the selection of diverse edges within the gradient space. Although diversity in the gradient space is effective for gradient-based optimizer, a challenge arises due to the high dimensionality of the gradient space, particularly when the model is large. To overcome this challenge, we compute the expected gradient of the loss function with respect to only the penultimate layer of the network, \(\nabla _{\theta _{out}}{{\mathcal {L}}}_{e_{ij}^{-}}\) , assuming it captures task-specific features. We begin to construct an optimal subset \(B_{N}^{*} \in B_{U}\) by initially (say, at \(k=0\) ) selecting the two edges with the most distinct gradients. Subsequently, we iteratively select the most dissimilar gradient edge from the selected subset using the maxmin optimization objective defined in Eq.  13 .

Here \(d_{E}\) represents the Euclidean distance between two vectors in the gradient space, consisting of \(\nabla _{\theta _{out}}{{\mathcal {L}}}_{e_{ij}^{-1}}\) , which denotes the gradient of the loss function \({{\mathcal {L}}}\) with respect to the penultimate layer of the network \(\theta _{out}\) . The process continues until we reach the allocated incremental training budget, \(|B_{N}^{*}| = K\) . The resulting optimal subset of negative edges, \(B_{N}^{*}\) , comprises negative edges that are both diverse and informative.

3.6.2 Positive Edge Sampling

Inspired by Curriculum Learning (CL), a technique mimicking certain aspects of human learning, we investigate its potential to enhance the performance and generalization of the node pair predictor model. Curriculum Learning involves presenting training data to the model in a purposeful order, starting with easier examples and gradually progressing to more challenging ones. We hypothesize that applying CL principles can benefit our node pair predictor model. By initially emphasizing the learning of simpler connections and leveraging prior knowledge, the model can effectively generalize to more complex connections during later stages of training. Although Active Learning (AL) and CL both involve estimating the utility of training samples, they differ in their approach to label availability. AL operates in scenarios where labels are unknown and estimates sample utility based on expected scores. In contrast, CL uses known labels to assess sample difficulty. For our model, we use one of the common approaches to define a difficulty score \(S_{CL}\) based on the model’s prediction confidence. The model’s higher prediction confidence indicates easier samples.

Here, \(S_{CL}(v_{i}, v_{j})\) indicates predictive uncertainty of an edge \(e_{ij}\) to be positive by an existing trained model \(\theta ^{m-1}\) at \((m-1)^{th}\) iteration. In summary, for hypothesis prediction using a large training dataset, Active Curriculum Learning provides a natural approach to sample an informative and diverse subset of high-quality samples, helping to alleviate the challenges associated with label bias.

4 Experimental setup

In this section, we present the experimental setup for our evaluation. We compare our proposed model, THiGER(-A), against several state-of-the-art (SOTA) methods to provide context for the empirical results on benchmark datasets. To ensure fair comparisons, we utilize publicly available baseline implementations and modify those as needed to align with our model’s configuration and input requirements. All experiments were conducted using Python. For the evaluation of the interaction datasets, we train all models on a single NVIDIA A10G GPU. In the case of the food-related biomedical dataset, we employ 4 NVIDIA V100 GPUs for model training. Notably, all models are trained on single machines. In our experiments, we consider graphs as undirected. The node attribute embedding dimension is set to \(d=128\) for all models evaluated. For baseline methods, we performed a parameter search on the learning rate and training steps, and we report the best results achieved. Our model is implemented in TensorFlow.

4.1 Datasets and model setup

Table 1 shows the statistics of the datasets used in this study. Unless explicitly mentioned, all methods, including our model, share the same initial node attributes provided by pretrained Node2Vec (Grover and Leskovec 2016 ). The pre-trained Node2vec embedding effectively captures the structural information of nodes in the training graph. In our proposed framework, the choice of a fixed node embedding is to enable the model capture the temporal evolution of network relations, given that the node embeddings are in the same vector space. While employing a dynamic node embedding framework may enhance results, it introduces complexities associated with aligning vector spaces across different timestamps. This aspect is deferred to future research. It is important to note that the Node2vec embeddings serve solely as initializations for the embedding layer, and the embedding vectors undergo fine-tuning during the learning process to further capture the dynamic evolution of node relationships. For models that solely learn embedding vectors for individual nodes, we represent the \(h_{i,j}\) of a given node pair as the concatenation of the embedding vectors for nodes \(\langle x_i, x_j \rangle\) .

4.1.1 Interaction datasets

We have restructured the datasets to align with our specific use case. We partition the edges in the temporal graphs into five distinct groups based on their temporal labels. For example, if a dataset is labeled up to 500 time units, we reorganize them as follows: \(\{0, \dots , 100\} \rightarrow 0\) , \(\{101, \dots , 200\} \rightarrow 1\) , \(\{201, \dots , 300\} \rightarrow 2\) , \(\{301, \dots , 400\} \rightarrow 3\) , and \(\{401, \dots , 500\} \rightarrow 4\) . These User-Item based datasets create bipartite graphs. For all inductive evaluations, we assume knowledge of three nearest node neighbors for each of the unseen nodes. Neighborhood information is updated after model training to incorporate this knowledge, with zero vectors assigned to new nodes.

4.1.2 Food-related biomedical temporal datasets

To construct the relationship graph, we extract sentences containing predefined entities (Genes, Diseases, Chemical Compounds, Nutrition, and Food Ingredients). We establish connections between two concepts that appear in the same sentence within any publication in the dataset. The time step for each relationship between concept pairs corresponds to the publication year when the first mention was identified (i.e., the oldest publication year among all the publications where the concepts are associated). We generate three datasets for evaluation based on concept pair domains: Ingredient, disease pairs, Ingredient, Chemical compound pairs, and all pairs (unfiltered). Graph statistics are provided in Table 1 . For training and testing sets, we divide the graph into 10-year intervals starting from 1940 (i.e., { \(\le 1940\) }, {1941–1950}, \(\dots\) , {2011–2020}). The splits \(\le\) 2020 are used for training, and the split {2021–2022} is used for testing. In accordance with the problem configuration in the interaction dataset, we update the neighborhood information and also assume knowledge of three nearest node neighbors pertaining to each of the unseen nodes for inductive evaluations.

4.1.3 Model setup & parameter tuning

Model Configuration: We employ a hierarchical encoder with \(N\lceil \log _{2} T \rceil\) layers, where N is a multiple of each hierarchical layer (i.e., with \(N-1\) encoder sublayers), and T represents the number of time steps input to each hierarchical encoder layer. In our experiments, we set the number of encoder layer multiples to \(N=2\) . We use 8 attention heads with 128 dimensional states. For the position-wise feed-forward networks, we use 512 dimensional inner states. For the activation function, we applied the Gaussian Error Linear Unit (GELU, Hendrycks and Gimpel 2016 ). We apply a dropout (Srivastava et al. 2014 ) to the output of each sub-layer with a rate of \(P_{drop} = 0.1\) .

Optimizer: Our models are trained using the AdamW optimizer (Loshchilov and Hutter 2017 ), with the following hyper-parameters: \(\beta _1 = 0.9\) , \(\beta _2 = 0.99\) , and \(\epsilon = 10^{-7}\) . We use a linear decay of the learning rate. We set the number of warmup steps to \(10\%\) of the number of train steps. We vary the learning rate with the size of the training data.

Time Embedding: We use Time2Vec (T2V, Kazemi et al. 2019 ) to generate time-step embeddings which encode the temporal sequence of the time steps. The T2V model is learned and updated during the model training.

Active learning: The size of subset \(B_U\) is twice the size of the optimal subset \(B^{*}\) . The model undergoes seven training rounds for the Wikipedia, Reddit, and Last FM datasets, while it is trained for three rounds for the food-related biomedical dataset (All, Ingredient-Disease, Ingredient-Chemical). Due to the large size of biomedical dataset, we limit the model training to only three rounds. However, we anticipate that increasing the number of training rounds will lead to further improvements in performance.

4.2 Evaluation metrics

In this study, we assess the efficacy of the models by employing the binary F1 score and average precision score (AP) as the performance metrics. The binary F1 score is defined as the harmonic mean of precision and recall, represented by the formula:

Here, precision denotes the ratio of true positive predictions to the total predicted positives, while recall signifies the ratio of true positive predictions to the total actual positives.

The average precision score is the weighted mean of precisions achieved using different thresholds, using the incremental change in recall from the previous threshold as weight:

where N is the total number of thresholds, \(P_{k}\) is the precision at cut-off k , and \(\Delta R_{k} = R_{k} - R_{k - 1}\) is a sequential change in the recall value. Our emphasis on positive predictions in the evaluations is driven by our preference for models that efficiently forecast future connections between pairs of nodes.

4.3 Method categories

We categorize the methods into two main groups based on their handling of temporal information:

Static Methods: These methods treat the graph as static data and do not consider the temporal aspect. The static methods under consideration include the Logistic regression model, GraphSAGE (Hamilton et al. 2017 ), and AGATHA (Sybrandt et al. 2020 ).

Temporal Methods: These state-of-the-art methods leverage temporal information to create more informative node representations. We evaluate the performance of our base model, THiGER, and the final model, THiGER-A, against the following temporal methods: CTDNE (Nguyen et al. 2018 ), TGN (Rossi et al. 2020 ), JODIE (Kumar et al. 2019 ), TNodeEmbed (Singer et al. 2019 ), DyRep (Trivedi et al. 2019 ), T-PAIR (Akujuobi et al. 2020b ), and TDE (Zhou et al. 2022 ).

5 Experiments

The performance of THiGER-A is rigorously assessed across multiple benchmark datasets, as presented in Tables 2 and 3 . The experimental evaluations are primarily geared toward two distinct objectives:

Assessing the model’s effectiveness in handling interaction datasets pertinent to temporal graph problems.

Evaluating the model’s proficiency in dealing with food-related biomedical datasets, specifically for predicting relationships between food-related concepts and other biomedical terms.

In Sects.  4.1.1 and 4.1.2 , a comprehensive overview of the used datasets is provided. Our evaluations encompass two fundamental settings:

Transductive setup: This scenario involves utilizing data from all nodes during model training.

Inductive setup: In this configuration, at least one node in each evaluated node pair has not been encountered during the model’s training phase.

These experiments are designed to rigorously assess THiGER-A’s performance across diverse datasets, offering insights into its capabilities under varying conditions and problem domains.

5.1 Quantitative evaluation: interaction temporal datasets

We assess the performance of our proposed model in the context of future interaction prediction (Rossi et al. 2020 ; Kumar et al. 2019 ). The datasets record interactions between users and items.

We evaluate the performance on three distinct datasets: (i) Reddit, (ii) LastFM, and (iii) Wikipedia, considering both transductive and inductive settings. In the transductive setting, THiGER-A outperforms other models across all datasets, except Wikipedia, where AGATHA exhibits significant superiority. Our analysis reveals that AGATHA’s advantage lies in its utilization of the entire graph for neighborhood and negative sampling, which gives it an edge over models using a subset of the graph due to computational constraints. This advantage is more evident in the transductive setup since AGATHA’s training strategy leans towards seen nodes. Nevertheless, THiGER-A consistently achieves comparable or superior performance even in the presence of AGATHA’s implicit bias. It is imperative to clarify that AGATHA was originally designed for purposes other than node-pair predictions. Nonetheless, we have adapted the algorithm to align with the node-pair configuration specifically for our research evaluations.

In the inductive setup, our method excels in the Wikipedia and Reddit datasets but lags behind some baselines in the LastFM dataset. Striking a balance between inductive and transductive performance, THiGER-A’s significant performance gain over THiGER underscores the effectiveness of the proposed incremental learning strategy. This advantage is particularly pronounced in the challenging inductive test setting.

5.2 Quantitative evaluation: food-related biomedical temporal datasets

This section presents the quantitative evaluation of our proposed model on temporal node pair (or “link”) prediction, explicitly focusing on food-related concept relationships extracted from scientific publications in the PMC dataset. The evaluation encompasses concept pairs from different domains, including Ingredient, Disease pairs (referred to as F-ID), Ingredient, Chemical Compound pairs (F-IC), and all food-related pairs (F-A). The statistical characteristics of the dataset are summarized in Table  1 .

Table  3 demonstrates that our model outperforms the baseline models in both inductive and transductive setups. The second-best performing model is AGATHA, which, as discussed in the previous section, exhibits certain advantages over alternative methods. It is noteworthy that the CTDNE method exhibits scalability issues with larger datasets.

An intriguing observation from this evaluation is that, aside from our proposed model, static methods outperform temporal methods on this dataset. Further investigation revealed that the data is more densely distributed toward the later time steps. Notably, a substantial increase in information occurs during the last time steps. Up to the year 2000, the average number of edges per time step is approximately 100, 000. However, this number surges to about 1 million in the time window from 2001 to 2010, followed by another leap to around 4 million in the 2011–2020 time step. This surge indicates a significant influx of knowledge in food-related research in recent years.

We hypothesize that while this influx is advantageous for static methods, it might adversely affect some temporal methods due to limited temporal information. To test this hypothesis, we conduct an incremental evaluation, illustrated in Fig.  4 , using two comparable link prediction methods (Logistic Regression and GraphSAGE) and the two best temporal methods (tNodeEmbed and THiGER). In this evaluation, we incrementally assess the transductive performance on testing pairs up to the year 2000. Specifically, we evaluate the model performance on the food dataset (F-A) in the time intervals 1961–1970 by using all available training data up to 1960, and similarly for subsequent time intervals.

From Fig.  4 , it is evident that temporal methods outperform static methods when the temporal data is more evenly distributed, i.e., when there is an incremental increase in temporal data. The sudden exponential increase in data during the later years biases the dataset towards the last time steps. However, THiGER consistently outperforms the baseline methods in the incremental evaluation, underscoring its robustness and flexibility.

Transductive F1 score of incremental prediction (per year) made by THiGER and three other baselines. The models are incrementally trained with data before the displayed evaluation time window

5.3 Ablation study

In this section, we conduct an ablation study to assess the impact of various sampling strategies on the base model’s performance. The results are presented in Table  4 , demonstrating the performance improvements achieved by the different versions of the THiGER model (-mean, -gin and -attn) for each dataset. Due to the much larger size of the food-related biomedical dataset, we conduct the ablation study only for the baseline datasets.

First, we investigate the influence of the active learning (AL)-based negative sampler on the base THiGER model. A comparison of the model’s performance with and without the AL-based negative sampler reveals significant improvements across all datasets. Notably, the performance gains are more pronounced in the challenging inductive test cases where at least one node of an edge is unseen in the training data. This underscores the effectiveness and generalizability of the AL-based learner for the hypothesis prediction model in the positive-unlabeled (PU) learning setup.

Next, we integrate curriculum learning (CL) as a positive data sampler, resulting in further enhancements to the base model. Similar to the AL-based sampling, the performance gains are more pronounced in the inductive test setting. The relatively minor performance improvement in the transductive case may be attributed to the limited room for enhancement in that specific context. Nevertheless, both AL alone and AL combined with CL enhance the base model’s performance and generalizability, particularly in the inductive test scenario.

Pair embedding visualization. The blue color denotes the true negative samples, the red points are false negative, the green points are true positive, and the purple points are false positive

5.4 Pair embedding visualization

In this section, we conduct a detailed analysis of the node pair embeddings generated by THiGER using the F-ID dataset. To facilitate visualization, we randomly select 900 pairs and employ t-SNE (Van der Maaten and Hinton 2008 ) to compare these embeddings with those generated by Node2Vec, as shown in Fig.  5 . We employ color-coding to distinguish between the observed labels and the predicted labels. Notably, we observe distinct differences in the learned embeddings. THiGER effectively separates positive and negative node pairs in the embedding space. True positives (denoted in green) and true negatives (denoted in blue) are further apart in the embedding space, while false positives (indicated in red) and false negatives (shown in purple) occupy an intermediate region. This observation aligns with the idea that unknown connections are not unequivocal in our application domain, possibly due to missing data or discoveries yet to be made.

5.5 Case study

To assess the predictive accuracy of our model, we conducted a detailed analysis using the entire available food-related biomedical temporal dataset. We collaborated with biologists to evaluate the correctness of the generated hypotheses. Unlike providing binary predictions (1 or 0), we take a probabilistic approach by assigning a probability score within the range of 0 to 1. This score reflects the likelihood of a connection existing between the predicted node pairs. Consequently, the process of ranking a set of relation predictions associated with a specific node is tantamount to ranking the corresponding predicted probabilities.

Using this methodology, we selected 402 node pairs and presented them to biomedical researchers for evaluation. The researchers sought hypotheses related to specific oils. Subsequently, we generated hypotheses representing potential future connections between the oil nodes and other nodes, resulting in a substantial list. Given the anticipated extensive list, we implemented a filtering process based on the associated probability scores. This enabled us to selectively identify predictions with high probabilities, which were then communicated to the biomedical researchers for evaluation. The evaluation encompassed two distinct approaches.

First, they conducted manual searches for references to the predicted positive node pairs in various biology texts, excluding our dataset. Their findings revealed relationships in 70 percent of the node pairs through literature searches and reviews.

Secondly, to explore cases where no direct relationship was apparent in existing literature, they randomly selected and analyzed three intriguing node pairs: (i) Flaxseed oil and Root caries , (ii) Benzoxazinoid and Gingelly oil , and (iii) Senile osteoporosis and Soybean oil .

5.5.1 Flaxseed oil and root caries

Root caries refers to a dental condition characterized by the decay and demineralization of tooth root surfaces. This occurs when tooth roots become exposed due to gum recession, allowing bacterial invasion and tooth structure erosion. While the scientific literature does not explicitly mention the use of flaxseed oil for root caries, it is well-established that flaxseed oil possesses antibacterial properties (Liu et al. 2022 ). These properties may inhibit bacterial species responsible for root caries. Furthermore, flaxseed oil is a rich source of omega-3 fatty acids and lignans, factors potentially relevant to this context. Interestingly, observational studies are investigating the oil’s effects on gingivitis (Deepika 2018 ).

5.5.2 Benzoxazinoid and gingelly oil

Benzoxazinoids are plant secondary metabolites synthesized in many monocotyledonous species and some dicotyledonous plants (Schullehner et al. 2008 ). Gingelly oil, derived from sesame seeds, originates from a dicotyledonous plant. In the biologists’ opinion, this concurrence suggests a valid basis for the hypothesized connection.

5.5.3 Senile osteoporosis and soybean oil

Senile osteoporosis is a subtype of osteoporosis occurring in older individuals due to age-related bone loss. Soybean oil, a common vegetable oil derived from soybeans, contains phytic acid (Anderson and Wolf 1995 ). Phytic acid is known to inhibit the absorption of certain minerals, including calcium, which is essential for bone strength (Lönnerdal et al. 1989 ). Again, in the experts’ opinion, this suggests a valid basis for a (unfortunately detrimental) connection between the oil and the health condition.

6 Conclusions

We introduce an innovative approach to tackle the hypothesis generation problem within the context of temporal graphs. We present THiGER, a novel transformer-based model designed for node pair prediction in temporal graphs. THiGER leverages a hierarchical framework to effectively capture and learn from temporal information inherent in such graphs. This framework enables efficient parallel temporal information aggregation. We also introduce THiGER-A, an incremental training strategy that enhances the model’s performance and generalization by training it on high-utility samples selected through active curriculum learning, particularly benefiting the challenging inductive test setting. Quantitative experiments and analyses demonstrate the efficiency and robustness of our proposed method when compared to various state-of-the-art approaches. Qualitative analyses illustrate its practical utility.

For future work, an enticing avenue involves incorporating additional node-pair relationship information from established biomedical and/or food-related knowledge graphs. In scientific research, specific topics often experience sudden exponential growth, leading to temporal data distribution imbalances. Another intriguing research direction, thus, is the study of the relationship between temporal data distribution and the performance of temporal graph neural network models. We plan to analyze the performance of several temporal GNN models across diverse temporal data distributions and propose model enhancement methods tailored to such scenarios.

Due to the vast scale of the publication graph, training the hypothesis predictor with all positive and negative edges is impractical and limits the model’s ability to generalize, especially when the input data is noisy. Thus, it is crucial to train the model selectively on a high-quality subset of the training data. Our work presents active curriculum learning as a promising approach for feasible and robust training for hypothesis predictors. However, a static strategy struggles to generalize well across different scenarios. An exciting direction for future research could be to develop dynamic policies for data sampling that automatically adapt to diverse applications. Furthermore, improving time complexity is a critical challenge, particularly for applications involving large datasets and models.

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Uchenna Akujuobi and Priyadarshini Kumari have contributed equally to this work.

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Sony AI, Barcelona, Spain

Uchenna Akujuobi, Samy Badreddine & Tarek R. Besold

Sony AI, Cupertino, USA

Priyadarshini Kumari

Sony AI, Tokyo, Japan

Jihun Choi & Kana Maruyama

The Systems Biology Institute, Tokyo, Japan

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U.A. and P.K. co-lead the reported work and the writing of the manuscript, J.C., S.B., K.M., and S.P. supported the work and the writing of the manuscript. T.B. supervised the work overall. All authors reviewed the manuscript and contributed to the revisions based on the reviewers’ feedback.

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Akujuobi, U., Kumari, P., Choi, J. et al. Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach. Artif Intell Rev 57 , 244 (2024). https://doi.org/10.1007/s10462-024-10885-1

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Know It All About Hypothesis Testing: A Simplified Guide

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• jaro education
• 13, August 2024

Hypothesis testing is one of the major methods in the grand scheme of statistics. It assists in deriving data-driven decisions, evaluating theories, and inferring meaningful conclusions from data. But what is hypothesis testing, and why is it so important in statistics? For those who want to learn and enhance their skills, the IIM Nagpur PG Certificate Programme in Data Science for Business Excellence and Innovation offers the perfect platform for learning from professionals, gaining hands-on experience, and moving up the value chain.

Introduction to Hypothesis Testing

Hypothesis testing is a means to make decisions about population properties based on the information derived from a sample. It simply provides a basis for the testing of assumptions or hypotheses made about a certain parameter in a population. Statement of the whole process involves a null hypothesis denoted by H0 and an alternative hypothesis denoted by H1, followed by the collection of data and subsequent analysis to see whether it favours the rejection of the null hypothesis in favour of the alternative.

In other words, hypothesis testing gives the answers to such questions as:

• Does a new drug improve patient outcomes?
• Is a new teaching method more effective than the old one?
• Does a marketing campaign increase sales?

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The Hypothesis Testing Process

The hypothesis testing process is quite logical and it contains several critical steps:

• State the Hypotheses: The hypotheses state the null hypothesis, H0, and the alternative hypothesis, H1. The null hypothesis normally represents a no-effect or no-difference statement, while the alternative represents what is to be proved.
• Select the Level of Significance (α): Probability of rejecting the null hypothesis when it is true. Common values are 0.05, 0.01, and 0.10.
• Choose the Right Test: Depending on the data and hypothesis, the test to be applied will be different, it could be anything from a t-test and chi-square test, among others.
• Gather and Summarise the Data: Get the data either through experiments or employing observation and then summarize the same through relevant statistics.
• Calculate the Test Statistic: The test statistic is calculated with the help of data. It expresses the probability that the sample data will be observed under the null hypothesis.
• Make a Decision: Compare the computed value of the test statistic to the critical value obtained from the statistical table or, using the p-value, accept or reject the null hypothesis.
• Draw a Conclusion: Using the results in the setting of the research question.

Hypothesis Testing Types

Within hypothesis testing, there are varieties that best fit different sorts of data and research questions. The main kinds include:

• Z-Test: Used with large samples, n > 30, with known population variance. 
• One-sample t-test: The sample mean is different from a known population mean.  
• Two-sample t-test: Used for comparison of the means of two independent groups.  
• Paired t-test: Comparison of means from the same group at different times.
• Chi-Square Test: This is carried out with categorical data to assess the probability that such a distribution would occur by chance. It includes the chi-square goodness of fit test, which shows whether a sample conforms to the population, and the chi-square test for independence, which determines whether two variables are independent of one another. 
• ANOVA: This is used to compare more than three groups’ means to determine if at least one mean of the groups differs from others.
• F-Test: It tests the hypothesis of equality of two population variances.

Examples in Real Life of Hypothesis Testing

Let us see how it works in practice with some examples.

Example 1: A/B Testing in Marketing

A company would like to know whether the new layout of a website, version B, provides more user engagement than the old layout, version A. The null hypothesis is that both versions have no difference in engagement. The alternative hypothesis is that the new layout leads to higher engagement. Performing an A/B test and examining the data will enable the company to either accept the new layout or not.

Example 2: Clinical Trials in Healthcare

A new medication in healthcare is tested in a clinical trial. Researchers want to establish if it can reduce blood pressure better than a previously existing one. The null hypothesis, H0, assumes no difference in blood pressure reduction between the new and the existing medications. The alternative hypothesis, H1, is that the new medication is more potent. The researchers can make an informed inference after administering different groups with the medications, measuring changes in blood pressure, and comparing such changes using a t-test.

Example 3: Quality Control in Manufacturing

A manufacturer wants to be certain that the true mean diameter for bolts manufactured by a machine is 5 mm. The null hypothesis is that the mean diameter is 5 mm. The alternative hypothesis is that the mean diameter is not 5 mm. The manufacturer conducts a hypothesis test based on a sample of bolts, measuring their diameters.

Significance of Hypothesis Testing in Business

Hypothesis testing is not merely an exercise alma mater; rather, it is something much needed for making a business decision. It may support enhancing or optimizing business strategies, operations, and customer satisfaction by rigorous testing of assumptions based on data-driven decisions. For instance, this would include areas such as product development, market research, or quality assurance.

This nuance in business matters a lot to decisions. It is useful in finding chances for innovation and optimizing marketing strategies to assure product quality. Hypothesis testing is used by businesses to test new ideas and concepts before full-scale implementation to avoid loss and risk, hence maximizing the chances of return maximization.

For instance, in the retail sector, one can use hypothesis testing to determine if the promotional campaign is effective. Comparing the sales data before and after a promotion will help the business learn if the promotion worked. In manufacturing, hypothesis testing can be used to ensure that production processes are within specified limits, to maintain the quality and consistency of the product.

Moreover, hypothesis testing is at the core of any financial analysis and forecasting . It helps the financial analyst measure investment performance, identify anomalies in financial data, and graph the future trends. It provides a firm statistical method to assist an informed decision that turns on empirical evidence rather than gut feelings or assumptions.

An in-depth look at types of hypothesis testing

Let us consider in greater detail some of the important hypothesis testing types enumerated earlier, and when and how they are used.

The Z-test is very useful in large samples, n > 30, with known population variances. It is frequently used in quality control and survey analysis. For example, suppose a company wants to compare the average customer satisfaction score from a large sample with the national average. A Z-test will tell whether their customer service passes or fails the national standard.

The T-test is useful when sample sizes are small and the population variance is unknown. There are three variants of the T-test, which makes it versatile in application:

• One-sample t-test: This is used to determine if the sample mean is significantly different from any known value. For instance, a school may use a one-sample t-test to determine if the average test score of the class differs from the national average.
• Two-sample t-test: This process compares the means of two independent groups. In a clinical trial, this test will be useful in comparing two different treatments.
• Paired t-test: The same group is measured at two different times. For example, weight loss of people before and after a diet program.

Chi-Square Test

Chi-square tests are used in categorical data. They are aimed at establishing whether the relationship between the two variables is significant. Chi-square tests may be applied in market research to establish whether some relationships exist, say between customer demographics and buying behavior. For example, it would tell if age groups have varying preferences for a given product or not.

ANOVA is used to compare three or more group means. The application of this statistical tool is thus very appropriate, especially in multiple treatment experiments. For instance, to establish which type of crop variety yields the highest under similar conditions, an agricultural scientist will apply the ANOVA to compare the yields of different crop varieties.

The F-test is carried out between the two variances to check if they are significantly different. One of the areas this test is commonly applied includes quality control processes, especially when it comes to comparing the variability of product measurements from different machines.

Practical Considerations in Hypothesis Testing

While hypothesis testing is an invaluable tool, there are some practical considerations which have to be borne in mind for effective and reliable results.

• Sample Size: The size of the sample in a study greatly influences the reliability of the test result. Inconclusive or misleading results are usually obtained when the sample size is small, and accurate estimates are always obtained using larger samples.
• Significance Level (): Selection of the appropriate level of significance is very important. A lower significance level will reduce the risk of type I errors, but on the other hand, increases type II errors or false negatives.
• Test Power: The power of the test refers to the probability of correct rejection of the null hypothesis where it is false. Higher power reduces the risk of type II errors and is obtained either by increasing the sample size or the effect size.
• Assumptions: There are different assumptions for the various hypothesis testing techniques. Some require normality and equal variances. In this respect, it is also important to check the assumptions before conducting the test to avoid erroneous conclusions.

Hypothesis testing is one of the critical parts of a statistical analysis and hence sets a clearly defined framework within which data-driven decisions can be made. Once you have grasped how the process and types of hypothesis testing work, you will be able to apply such methods to real-world problems in ways that can drive business excellence and innovation.

It isn’t a theoretical concept but a practical tool that enables better decision-making across many domains. Be it healthcare, marketing, or manufacturing, it is understanding hypothesis testing that, with the application of this understanding, leads toward more informed and data-driven decision-making to propel the organization in the proper direction.

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