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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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• 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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Formulating Hypotheses for Different Study Designs

Durga prasanna misra.

1 Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.

Armen Yuri Gasparyan

2 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, UK.

Olena Zimba

3 Department of Internal Medicine #2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Vikas Agarwal

George d. kitas.

5 Centre for Epidemiology versus Arthritis, University of Manchester, Manchester, UK.

Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports. Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).

Graphical Abstract

DEFINING WORKING AND STANDALONE SCIENTIFIC HYPOTHESES

Science is the systematized description of natural truths and facts. Routine observations of existing life phenomena lead to the creative thinking and generation of ideas about mechanisms of such phenomena and related human interventions. Such ideas presented in a structured format can be viewed as hypotheses. After generating a hypothesis, it is necessary to test it to prove its validity. Thus, hypothesis can be defined as a proposed mechanism of a naturally occurring event or a proposed outcome of an intervention. 1 , 2

Hypothesis testing requires choosing the most appropriate methodology and adequately powering statistically the study to be able to “prove” or “disprove” it within predetermined and widely accepted levels of certainty. This entails sample size calculation that often takes into account previously published observations and pilot studies. 2 , 3 In the era of digitization, hypothesis generation and testing may benefit from the availability of numerous platforms for data dissemination, social networking, and expert validation. Related expert evaluations may reveal strengths and limitations of proposed ideas at early stages of post-publication promotion, preventing the implementation of unsupported controversial points. 4

Thus, hypothesis generation is an important initial step in the research workflow, reflecting accumulating evidence and experts' stance. In this article, we overview the genesis and importance of scientific hypotheses and their relevance in the era of the coronavirus disease 2019 (COVID-19) pandemic.

DO WE NEED HYPOTHESES FOR ALL STUDY DESIGNS?

Broadly, research can be categorized as primary or secondary. In the context of medicine, primary research may include real-life observations of disease presentations and outcomes. Single case descriptions, which often lead to new ideas and hypotheses, serve as important starting points or justifications for case series and cohort studies. The importance of case descriptions is particularly evident in the context of the COVID-19 pandemic when unique, educational case reports have heralded a new era in clinical medicine. 5

Case series serve similar purpose to single case reports, but are based on a slightly larger quantum of information. Observational studies, including online surveys, describe the existing phenomena at a larger scale, often involving various control groups. Observational studies include variable-scale epidemiological investigations at different time points. Interventional studies detail the results of therapeutic interventions.

Secondary research is based on already published literature and does not directly involve human or animal subjects. Review articles are generated by secondary research. These could be systematic reviews which follow methods akin to primary research but with the unit of study being published papers rather than humans or animals. Systematic reviews have a rigid structure with a mandatory search strategy encompassing multiple databases, systematic screening of search results against pre-defined inclusion and exclusion criteria, critical appraisal of study quality and an optional component of collating results across studies quantitatively to derive summary estimates (meta-analysis). 6 Narrative reviews, on the other hand, have a more flexible structure. Systematic literature searches to minimise bias in selection of articles are highly recommended but not mandatory. 7 Narrative reviews are influenced by the authors' viewpoint who may preferentially analyse selected sets of articles. 8

In relation to primary research, case studies and case series are generally not driven by a working hypothesis. Rather, they serve as a basis to generate a hypothesis. Observational or interventional studies should have a hypothesis for choosing research design and sample size. The results of observational and interventional studies further lead to the generation of new hypotheses, testing of which forms the basis of future studies. Review articles, on the other hand, may not be hypothesis-driven, but form fertile ground to generate future hypotheses for evaluation. Fig. 1 summarizes which type of studies are hypothesis-driven and which lead on to hypothesis generation.

STANDARDS OF WORKING AND SCIENTIFIC HYPOTHESES

A review of the published literature did not enable the identification of clearly defined standards for working and scientific hypotheses. It is essential to distinguish influential versus not influential hypotheses, evidence-based hypotheses versus a priori statements and ideas, ethical versus unethical, or potentially harmful ideas. The following points are proposed for consideration while generating working and scientific hypotheses. 1 , 2 Table 1 summarizes these points.

Evidence-based data

A scientific hypothesis should have a sound basis on previously published literature as well as the scientist's observations. Randomly generated (a priori) hypotheses are unlikely to be proven. A thorough literature search should form the basis of a hypothesis based on published evidence. 7

Unless a scientific hypothesis can be tested, it can neither be proven nor be disproven. Therefore, a scientific hypothesis should be amenable to testing with the available technologies and the present understanding of science.

Supported by pilot studies

If a hypothesis is based purely on a novel observation by the scientist in question, it should be grounded on some preliminary studies to support it. For example, if a drug that targets a specific cell population is hypothesized to be useful in a particular disease setting, then there must be some preliminary evidence that the specific cell population plays a role in driving that disease process.

Testable by ethical studies

The hypothesis should be testable by experiments that are ethically acceptable. 9 For example, a hypothesis that parachutes reduce mortality from falls from an airplane cannot be tested using a randomized controlled trial. 10 This is because it is obvious that all those jumping from a flying plane without a parachute would likely die. Similarly, the hypothesis that smoking tobacco causes lung cancer cannot be tested by a clinical trial that makes people take up smoking (since there is considerable evidence for the health hazards associated with smoking). Instead, long-term observational studies comparing outcomes in those who smoke and those who do not, as was performed in the landmark epidemiological case control study by Doll and Hill, 11 are more ethical and practical.

Balance between scientific temper and controversy

Novel findings, including novel hypotheses, particularly those that challenge established norms, are bound to face resistance for their wider acceptance. Such resistance is inevitable until the time such findings are proven with appropriate scientific rigor. However, hypotheses that generate controversy are generally unwelcome. For example, at the time the pandemic of human immunodeficiency virus (HIV) and AIDS was taking foot, there were numerous deniers that refused to believe that HIV caused AIDS. 12 , 13 Similarly, at a time when climate change is causing catastrophic changes to weather patterns worldwide, denial that climate change is occurring and consequent attempts to block climate change are certainly unwelcome. 14 The denialism and misinformation during the COVID-19 pandemic, including unfortunate examples of vaccine hesitancy, are more recent examples of controversial hypotheses not backed by science. 15 , 16 An example of a controversial hypothesis that was a revolutionary scientific breakthrough was the hypothesis put forth by Warren and Marshall that Helicobacter pylori causes peptic ulcers. Initially, the hypothesis that a microorganism could cause gastritis and gastric ulcers faced immense resistance. When the scientists that proposed the hypothesis themselves ingested H. pylori to induce gastritis in themselves, only then could they convince the wider world about their hypothesis. Such was the impact of the hypothesis was that Barry Marshall and Robin Warren were awarded the Nobel Prize in Physiology or Medicine in 2005 for this discovery. 17 , 18

DISTINGUISHING THE MOST INFLUENTIAL HYPOTHESES

Influential hypotheses are those that have stood the test of time. An archetype of an influential hypothesis is that proposed by Edward Jenner in the eighteenth century that cowpox infection protects against smallpox. While this observation had been reported for nearly a century before this time, it had not been suitably tested and publicised until Jenner conducted his experiments on a young boy by demonstrating protection against smallpox after inoculation with cowpox. 19 These experiments were the basis for widespread smallpox immunization strategies worldwide in the 20th century which resulted in the elimination of smallpox as a human disease today. 20

Other influential hypotheses are those which have been read and cited widely. An example of this is the hygiene hypothesis proposing an inverse relationship between infections in early life and allergies or autoimmunity in adulthood. An analysis reported that this hypothesis had been cited more than 3,000 times on Scopus. 1

LESSONS LEARNED FROM HYPOTHESES AMIDST THE COVID-19 PANDEMIC

The COVID-19 pandemic devastated the world like no other in recent memory. During this period, various hypotheses emerged, understandably so considering the public health emergency situation with innumerable deaths and suffering for humanity. Within weeks of the first reports of COVID-19, aberrant immune system activation was identified as a key driver of organ dysfunction and mortality in this disease. 21 Consequently, numerous drugs that suppress the immune system or abrogate the activation of the immune system were hypothesized to have a role in COVID-19. 22 One of the earliest drugs hypothesized to have a benefit was hydroxychloroquine. Hydroxychloroquine was proposed to interfere with Toll-like receptor activation and consequently ameliorate the aberrant immune system activation leading to pathology in COVID-19. 22 The drug was also hypothesized to have a prophylactic role in preventing infection or disease severity in COVID-19. It was also touted as a wonder drug for the disease by many prominent international figures. However, later studies which were well-designed randomized controlled trials failed to demonstrate any benefit of hydroxychloroquine in COVID-19. 23 , 24 , 25 , 26 Subsequently, azithromycin 27 , 28 and ivermectin 29 were hypothesized as potential therapies for COVID-19, but were not supported by evidence from randomized controlled trials. The role of vitamin D in preventing disease severity was also proposed, but has not been proven definitively until now. 30 , 31 On the other hand, randomized controlled trials identified the evidence supporting dexamethasone 32 and interleukin-6 pathway blockade with tocilizumab as effective therapies for COVID-19 in specific situations such as at the onset of hypoxia. 33 , 34 Clues towards the apparent effectiveness of various drugs against severe acute respiratory syndrome coronavirus 2 in vitro but their ineffectiveness in vivo have recently been identified. Many of these drugs are weak, lipophilic bases and some others induce phospholipidosis which results in apparent in vitro effectiveness due to non-specific off-target effects that are not replicated inside living systems. 35 , 36

Another hypothesis proposed was the association of the routine policy of vaccination with Bacillus Calmette-Guerin (BCG) with lower deaths due to COVID-19. This hypothesis emerged in the middle of 2020 when COVID-19 was still taking foot in many parts of the world. 37 , 38 Subsequently, many countries which had lower deaths at that time point went on to have higher numbers of mortality, comparable to other areas of the world. Furthermore, the hypothesis that BCG vaccination reduced COVID-19 mortality was a classic example of ecological fallacy. Associations between population level events (ecological studies; in this case, BCG vaccination and COVID-19 mortality) cannot be directly extrapolated to the individual level. Furthermore, such associations cannot per se be attributed as causal in nature, and can only serve to generate hypotheses that need to be tested at the individual level. 39

IS TRADITIONAL PEER REVIEW EFFICIENT FOR EVALUATION OF WORKING AND SCIENTIFIC HYPOTHESES?

Traditionally, publication after peer review has been considered the gold standard before any new idea finds acceptability amongst the scientific community. Getting a work (including a working or scientific hypothesis) reviewed by experts in the field before experiments are conducted to prove or disprove it helps to refine the idea further as well as improve the experiments planned to test the hypothesis. 40 A route towards this has been the emergence of journals dedicated to publishing hypotheses such as the Central Asian Journal of Medical Hypotheses and Ethics. 41 Another means of publishing hypotheses is through registered research protocols detailing the background, hypothesis, and methodology of a particular study. If such protocols are published after peer review, then the journal commits to publishing the completed study irrespective of whether the study hypothesis is proven or disproven. 42 In the post-pandemic world, online research methods such as online surveys powered via social media channels such as Twitter and Instagram might serve as critical tools to generate as well as to preliminarily test the appropriateness of hypotheses for further evaluation. 43 , 44

Some radical hypotheses might be difficult to publish after traditional peer review. These hypotheses might only be acceptable by the scientific community after they are tested in research studies. Preprints might be a way to disseminate such controversial and ground-breaking hypotheses. 45 However, scientists might prefer to keep their hypotheses confidential for the fear of plagiarism of ideas, avoiding online posting and publishing until they have tested the hypotheses.

SUGGESTIONS ON GENERATING AND PUBLISHING HYPOTHESES

Publication of hypotheses is important, however, a balance is required between scientific temper and controversy. Journal editors and reviewers might keep in mind these specific points, summarized in Table 2 and detailed hereafter, while judging the merit of hypotheses for publication. Keeping in mind the ethical principle of primum non nocere, a hypothesis should be published only if it is testable in a manner that is ethically appropriate. 46 Such hypotheses should be grounded in reality and lend themselves to further testing to either prove or disprove them. It must be considered that subsequent experiments to prove or disprove a hypothesis have an equal chance of failing or succeeding, akin to tossing a coin. A pre-conceived belief that a hypothesis is unlikely to be proven correct should not form the basis of rejection of such a hypothesis for publication. In this context, hypotheses generated after a thorough literature search to identify knowledge gaps or based on concrete clinical observations on a considerable number of patients (as opposed to random observations on a few patients) are more likely to be acceptable for publication by peer-reviewed journals. Also, hypotheses should be considered for publication or rejection based on their implications for science at large rather than whether the subsequent experiments to test them end up with results in favour of or against the original hypothesis.

Hypotheses form an important part of the scientific literature. The COVID-19 pandemic has reiterated the importance and relevance of hypotheses for dealing with public health emergencies and highlighted the need for evidence-based and ethical hypotheses. A good hypothesis is testable in a relevant study design, backed by preliminary evidence, and has positive ethical and clinical implications. General medical journals might consider publishing hypotheses as a specific article type to enable more rapid advancement of science.

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

Author Contributions:

• Data curation: Gasparyan AY, Misra DP, Zimba O, Yessirkepov M, Agarwal V, Kitas GD.

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Exploring Research Question and Hypothesis Examples: A Comprehensive Guide

This comprehensive guide explores the intricacies of formulating research questions and hypotheses across various academic disciplines. By delving into examples and methodological approaches, the article aims to provide scholars and researchers with the tools necessary to develop robust and effective research frameworks. Understanding and crafting well-formed research questions and hypotheses are pivotal in conducting meaningful research that can significantly contribute to knowledge within a field.

Key Takeaways

• Understand the fundamental differences and connections between research questions and hypotheses.
• Learn how to craft effective and precise research questions that guide the research process.
• Explore various types of hypotheses and methods for testing and refining them.
• Examine practical examples of research questions and hypotheses across multiple disciplines.
• Gain insights into the impact of well-constructed research questions and hypotheses on research outcomes, academic publishing, and grant applications.

Understanding the Fundamentals of Research Questions and Hypotheses

Defining research questions.

Research questions are the backbone of any scholarly inquiry, guiding you through the exploration of your chosen topic. They help you focus your study and determine the direction of your research. A well-crafted research question should be clear, focused, and answerable within the constraints of your study.

Characteristics of a Strong Hypothesis

A strong hypothesis provides a specific, testable prediction about the expected outcomes of your research. It is not merely a guess but is grounded in existing literature and theory. To develop a robust hypothesis, consider the variables involved and ensure that it is feasible to test them within your study's design.

Interrelation Between Research Questions and Hypotheses

Understanding the interrelation between research questions and hypotheses is crucial for structuring your research effectively. Your hypothesis should directly address the gap in the literature highlighted by your research question, providing a clear pathway for investigation. This alignment ensures that your study can contribute valuable insights to your field.

Crafting Effective Research Questions

Identifying the purpose.

To craft an effective research question , you must first identify the purpose of your study. This involves understanding what you aim to discover or elucidate through your research. Ask yourself what the core of your inquiry is and what outcomes you hope to achieve. This clarity will guide your entire research process, ensuring that your question is not only relevant but also deeply rooted in your specific academic or practical goals.

Scope and Limitations

It's crucial to define the scope and limitations of your research early on. This helps in setting realistic boundaries and expectations for your study. Consider factors such as time, resources, and the breadth of the subject area. Narrowing down your focus to a manageable scope can prevent the common pitfall of an overly broad or vague question, which can dilute the impact of your findings.

Formulating Questions that Drive Inquiry

The final step in crafting your research question is formulating it in a way that drives inquiry. This means your question should be clear, concise, and structured to prompt detailed investigation and critical analysis. It should challenge existing knowledge and push the boundaries of what is already known. Utilizing strategies like the Thesis Dialogue Blueprint or the Research Proposal Compass can be instrumental in refining your question to ensure it is both innovative and feasible.

Developing Hypotheses in Research

From research questions to hypotheses.

When you transition from research questions to hypotheses, you are essentially moving from what you want to know to what you predict will happen. This shift involves formulating a specific, testable prediction that directly stems from your initial question. Ensure your hypothesis is directly linked to and derived from your research question to maintain a coherent research strategy.

Types of Hypotheses

There are several types of hypotheses you might encounter, including simple, complex, directional, nondirectional, associative, causal, null, and alternative. Each type serves a different purpose and is chosen based on the specifics of the research question and the nature of the study. For instance, a null hypothesis might be used to test the effectiveness of a new teaching method compared to the standard.

Testing and Refining Hypotheses

Testing your hypothesis is a critical step in the research process. This phase involves collecting data, conducting experiments, or utilizing other research methods to determine the validity of your hypothesis. After testing, you may find that your hypothesis needs refining or even reformation based on the outcomes. This iterative process is essential for narrowing down the most accurate explanation or prediction for your research question.

Examples of Research Questions in Various Disciplines

Humanities and social sciences.

In the realm of Humanities and Social Sciences, research questions often explore cultural, social, historical, or philosophical aspects. How does gender representation in 20th-century American literature reflect broader social changes? This question not only seeks to uncover specific literary trends but also ties them to societal shifts, offering a rich field for analysis.

Natural Sciences

Research questions in the Natural Sciences are typically aimed at understanding natural phenomena or solving specific scientific problems. A common question might be, What are the effects of plastic pollutants on marine biodiversity? This inquiry highlights the environmental concerns and seeks empirical data to understand the impact.

Applied Sciences

In Applied Sciences, the focus is often on improving technology or engineering solutions. A pertinent question could be, How can renewable energy sources be integrated into existing power grids? This question addresses the practical challenges and potential innovations in energy systems, crucial for advancing sustainable technologies.

Analyzing Hypothesis Examples Across Fields

Case studies in psychology.

In psychology, hypotheses often explore the causal relationships between cognitive functions and behaviors. Consider how a hypothesis might predict the impact of stress on memory recall . By examining various case studies, you can see how hypotheses are specifically tailored to address intricate psychological phenomena.

Experimental Research in Biology

Biology experiments frequently test hypotheses about physiological processes or genetic information. For instance, a hypothesis might propose that a specific gene influences plant growth rates. Through rigorous testing, these hypotheses contribute significantly to our understanding of biological systems.

Field Studies in Environmental Science

Field studies in environmental science provide a rich ground for testing hypotheses related to ecosystem dynamics and conservation strategies. A common hypothesis might explore the effects of human activity on biodiversity. These studies often involve complex data collection and analysis, highlighting the interrelation between empirical evidence and theoretical predictions.

Methodological Approaches to Formulating Hypotheses

Quantitative vs. qualitative research.

When you embark on hypothesis formulation, understanding the distinction between quantitative and qualitative research methodologies is crucial. Quantitative research focuses on numerical data and statistical analysis, ideal for hypotheses that require measurable evidence. In contrast, qualitative research delves into thematic and descriptive data, providing depth and context to hypotheses that explore behaviors, perceptions, and experiences.

The Role of Theoretical Frameworks

Theoretical frameworks serve as the backbone for developing robust hypotheses. They provide a structured way to align your hypothesis with existing knowledge. By integrating theories and models relevant to your study, you ensure that your hypothesis has a solid foundation and aligns with established academic thought.

Utilizing Existing Literature to Form Hypotheses

A thorough review of existing literature is indispensable for crafting a well-informed hypothesis. This process not only highlights gaps in current research but also allows you to build on the work of others. By synthesizing findings from previous studies, you can formulate hypotheses that are both innovative and grounded in academic precedent.

Evaluating the Impact of Well-Formed Research Questions and Hypotheses

On research outcomes.

Understanding the impact of well-formed research questions and hypotheses on research outcomes is crucial. Well-crafted questions and hypotheses serve as a framework that guides the entire research process , ensuring that the study remains focused and relevant. They help in defining the scope of the study and in identifying the variables that need to be measured, thus directly influencing the validity and reliability of the research findings.

In Academic Publishing

The role of well-defined research questions and hypotheses extends beyond the research process into the realm of academic publishing. A clear hypothesis provides a strong foundation for the research paper, enhancing its chances of acceptance in prestigious journals. The clarity and direction afforded by a solid hypothesis make the research more appealing to a scholarly audience, potentially increasing citation rates and academic recognition.

In Grant Applications

When applying for research grants, the clarity of your research questions and hypotheses can significantly impact the decision-making process of funding bodies. A well-articulated hypothesis demonstrates a clear vision and a structured approach to addressing a specific issue, which can be crucial in securing funding. Grant reviewers often look for proposals that promise substantial contributions to the field, and a strong hypothesis can be a key factor in showcasing the potential impact of your research.

In our latest article, 'Evaluating the Impact of Well-Formed Research Questions and Hypotheses,' we delve into the crucial role that precise questions and hypotheses play in academic research. Understanding this can significantly enhance your thesis writing process. For a deeper exploration and practical tools to apply these concepts, visit our website and discover how our Thesis Action Plan can transform your academic journey. Don't miss out on our special offers tailored just for you!

In this comprehensive guide, we have explored various examples of research questions and hypotheses, shedding light on their significance and application in academic research. Understanding the distinction between a research question and a hypothesis, as well as knowing how to effectively formulate them, is crucial for conducting methodical and impactful studies. By examining different scenarios and examples, this guide aims to equip researchers with the knowledge to craft well-defined research questions and hypotheses that can drive meaningful investigations and contribute to the broader field of knowledge. As we continue to delve into the intricacies of research design, it is our hope that this guide serves as a valuable resource for both novice and experienced researchers in their scholarly endeavors.

Frequently Asked Questions

What is a research question.

A research question is a clearly defined query that guides a scientific or academic study. It sets the scope and focus of the research by asking about a specific phenomenon or issue.

How does a hypothesis differ from a research question?

A hypothesis is a specific, testable prediction about what will happen in a study based on prior knowledge or theory, while a research question is an open query that guides the direction of the investigation.

What are the characteristics of a strong hypothesis?

A strong hypothesis is clear, testable, based on existing knowledge, and it states an expected relationship between variables.

How can research questions and hypotheses interrelate?

Research questions define the scope of inquiry, while hypotheses provide a specific prediction about the expected outcomes that can be tested through research methods.

What should be considered when formulating a research question?

When formulating a research question, consider clarity, focus, relevance, and the feasibility of answering the question through available research methods.

Why is it important to have a well-formed hypothesis?

A well-formed hypothesis directs the research process, allows for clear testing of assumptions, and helps in drawing meaningful conclusions that can contribute to the body of knowledge.

How Do You Write a Hypothesis? Step-by-Step Instructions

How do you write an hypothesis detailed explanation and examples, how to ask a research question: strategies and tips, how do you write a good hypothesis tips and techniques, how to write a proposal for a thesis: a comprehensive guide.

How Do You Write a Hypothesis for a Research Paper? Step-by-Step Guide

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Formulating Research Hypothesis and Objective

• First Online: 01 March 2024

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• Animesh Hazari 2  

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Formulating a research hypothesis and objectives is the first and foremost step in any research process as they provide a clear direction and purpose for your study. In this chapter, we shall learn about formulating an ideal research hypothesis and objectives. Formulation and development of the hypothesis and objectives take place under the following key steps:

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Hazari, A. (2023). Formulating Research Hypothesis and Objective. In: Research Methodology for Allied Health Professionals. Springer, Singapore. https://doi.org/10.1007/978-981-99-8925-6_4

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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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16 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

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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 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).

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.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

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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• Formulation of Hypothesis

Children who spend more time playing outside are more likely to be imaginative. What do you think this statement is an example of in terms of scientific research ? If you guessed a hypothesis, then you'd be correct. The formulation of hypotheses is a fundamental step in psychology research.

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• Approaches in Psychology
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• First, we will discuss the importance of hypotheses in research.
• We will then cover formulating hypotheses in research, including the steps in the formulation of hypotheses in research methodology.
• We will provide examples of hypotheses in research throughout the explanation.
• Finally, we will delve into the different types of hypotheses in research.

What is a Hypothesis?

The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research . To be classed as scientific research , it must be observable, valid, reliable and follow a standardised procedure.

One of the important steps in scientific research is to formulate a hypothesis before starting the study procedure.

The hypothesis is a predictive, testable statement predicting the outcome and the results the researcher expects to find.

The hypothesis provides a summary of what direction, if any, is taken to investigate a theory.

In scientific research, there is a criterion that hypotheses need to be met to be regarded as acceptable.

If a hypothesis is disregarded, the research may be rejected by the community of psychology researchers.

Importance of Hypothesis in Research

The purpose of including hypotheses in psychology research is:

• To provide a summary of the research, how it will be investigated, and what is expected to be found.
• To provide an answer to the research question.

When carrying out research, researchers first investigate the research area they are interested in. From this, researchers are required to identify a gap in the literature.

Filling the gap essentially means finding what previous work has not been explained yet, investigated to a sufficient degree, or simply expanding or further investigating a theory if doubt exists.

The researcher then forms a research question that the researcher will attempt to answer in their study.

Remember, the hypothesis is a predictive statement of what is expected to happen when testing the research question.

The hypothesis can be used for later data analysis. This includes inferential tests such as hypothesis testing and identifying if statistical findings are significant.

Steps in the Formulation of Hypothesis in Research Methodology

Researchers must follow certain steps to formulate testable hypotheses when conducting research.

Overall, the researcher has to consider the direction of the research, i.e. will it be looking for a difference caused by independent variables ? Or will it be more concerned with the correlation between variables?

All researchers will likely complete the following.

• Investigating background research in the area of interest.
• Formulating or investigating a theory.
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works.

The above steps are used to formulate testable hypotheses.

The Formulation of Testable Hypotheses

The hypothesis is important in research as it indicates what and how a variable will be investigated.

The hypothesis essentially summarises what and how something will be investigated. This is important as it ensures that the researcher has carefully planned how the research will be done, as the researchers have to follow a set procedure to conduct research.

This is known as the scientific method.

Formulating Hypotheses in Research

When formulating hypotheses, things that researchers should consider are:

Types of Hypotheses in Research

Researchers can propose different types of hypotheses when carrying out research.

The following research scenario will be discussed to show examples of each type of hypothesis that the researchers could use. "A research team was investigating whether memory performance is affected by depression ."

The identified independent variable is the severity of depression scores, and the dependent variable is the scores from a memory performance task.

The null hypothesis predicts that the results will show no or little effect. The null hypothesis is a predictive statement that researchers use when it is thought that the IV will not influence the DV.

In this case, the null hypothesis would be there will be no difference in memory scores on the MMSE test of those who are diagnosed with depression and those who are not.

An alternative hypothesis is a predictive statement used when it is thought that the IV will influence the DV. The alternative hypothesis is also called a non-directional, two-tailed hypothesis, as it predicts the results can go either way, e.g. increase or decrease.

The example in this scenario is there will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

The directional alternative hypothesis states how the IV will influence the DV, identifying a specific direction, such as if there will be an increase or decrease in the observed results.

The example in this scenario is people with low depressive scores will perform better in the memory performance task than people who score higher in depressive symptoms.

Example Hypothesis in Research

To summarise, let's look at an example of a straightforward hypothesis that indicates the relationship between two variables: the independent and the dependent.

If you stay up late, you will feel tired the following day; the more caffeine you drink, the harder you find it to fall asleep, or the more sunlight plants get, the taller they will grow.

Formulation of Hypothesis - Key Takeaways

• The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research. One of the important steps in scientific research is to create a hypothesis.
• The hypothesis is a predictive, testable statement concerning the outcome/results that the researcher expects to find.
• Hypotheses are needed in research to provide a summary of what the research is, how to investigate a theory and what is expected to be found, and to provide an answer to the research question so that the hypothesis can be used for later data analysis.
• There are requirements for the formulation of testable hypotheses. The hypotheses should identify and operationalise the IV and DV. In addition, they should describe the nature of the relationship between the IV and DV.
• There are different types of hypotheses: Null hypothesis, Alternative hypothesis (this is also known as the non-directional, two-tailed hypothesis), and Directional hypothesis (this is also known as the one-tailed hypothesis).

Flashcards in Formulation of Hypothesis 18

What type of hypothesis matches the following definition. A predictive statement that researchers use when it is thought that the IV will not influence the DV.

Null hypothesis 

What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV. But, the hypothesis does not state how the IV will influence the DV. 

Alternative hypothesis 

What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV, and states how it will influence the DV. 

Directional, alternative hypothesis 

Which type of hypothesis is also known as a two-tailed hypothesis? 

What type of hypothesis is the following example. There will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

What type of hypothesis is the following example. There will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

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Frequently Asked Questions about Formulation of Hypothesis

What are the 3 types of hypotheses?

The three types of hypotheses are:

• Null hypothesis 
• Alternative hypothesis 
• Directional/non-directional hypothesis 

What is an example of a hypothesis in psychology?

An example of a null hypothesis in psychology is, there will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

What are the steps in formulating a hypothesis?

All researchers will likely complete the following

• Investigating background research in the area of interest 
• Formulating or investigating a theory 
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works 

What is formulation of hypothesis in research? 

The formulation of a hypothesis in research is when the researcher formulates a predictive statement of what is expected to happen when testing the research question based on background research.

How to formulate  null and alternative hypothesis?

When formulating a null hypothesis the researcher would state a prediction that they expect to see no difference in the dependent variable when the independent variable changes or is manipulated. Whereas, when using an alternative hypothesis then it would be predicted that there will be a change in the dependent variable. The researcher can state in which direction they expect the results to go. 

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AI Generator

When doing a research action plan students in school would know that the first thing to do is to know your topic well enough. From expecting science projects to work based on your predictions and the results that may have been quite the opposite from how you depicted them. This also rings true in businesses. There is a term for that and it is often associated with the subject Science, but can also be associated with business . Scientific method  or a hypothesis.

What Is a Hypothesis?

A hypothesis is a scientific wild guess, a prediction in research . A wild guess, a say from someone without any known proof.  A hypothesis can also mean a scientific, educated guess that most scientists and researchers do before planning out or doing experiments to check if their guesses or their scientific ideas based on their topics are exact or correct.

Hypothesis Format

A well-structured hypothesis is crucial for guiding scientific research. Here’s a detailed format for writing a hypothesis, along with examples for each step:

1. Start with a Research Question

Before writing a hypothesis, begin with a clear and concise research question . This question identifies the focus of your study.

Example Research Question: Does the amount of daily exercise affect weight loss?

2. Identify the Variables

Identify the independent and dependent variables in your research question.

• Independent Variable: The variable you manipulate (e.g., amount of daily exercise).
• Dependent Variable: The variable you measure (e.g., weight loss).

3. Formulate the Hypothesis

Use the identified variables to create a testable statement . This statement should clearly express the expected relationship between the variables.

• If [independent variable], then [dependent variable].
• [Independent variable] will [effect] [dependent variable].

Directional vs. Non-Directional Hypothesis:

• Specifies the direction of the expected relationship.
• Does not specify the direction of the expected relationship, only that a relationship exists.

4. Example Hypotheses Using the Format

Research question: does caffeine affect cognitive performance, if-then statement:.

• Example: If individuals consume caffeine, then their cognitive performance will improve.

Direct Statement:

• Example: Caffeine consumption will improve cognitive performance.

Null Hypothesis (H0):

• Example: There is no significant effect of caffeine consumption on cognitive performance.

Alternative Hypothesis (H1):

• Example: There is a significant effect of caffeine consumption on cognitive performance.

Directional Hypothesis:

Non-directional hypothesis:.

• Example: There is a relationship between caffeine consumption and cognitive performance.

5. Refining the Hypothesis

Ensure that your hypothesis is specific, measurable, and testable. Avoid vague terms and focus on a single independent and dependent variable.

Hypothesis Examples in Research

A hypothesis is a statement that predicts the relationship between variables. It serves as a foundation for research by providing a clear focus and direction for experiments and data analysis . Here are examples of hypotheses from various fields of research:

Research Question:

Does sunlight exposure affect plant growth?

Hypotheses:

• Null Hypothesis (H0): There is no significant difference in plant growth between plants exposed to sunlight and those kept in the shade.
• Alternative Hypothesis (H1): Plants exposed to sunlight grow taller than those kept in the shade.
• Directional Hypothesis: Increased sunlight exposure will lead to increased plant growth.
• If-Then Statement: If plants are exposed to more sunlight, then they will grow taller.

2. Psychology

Does sleep duration affect memory retention?

• Null Hypothesis (H0): There is no significant difference in memory retention between individuals who sleep for 8 hours and those who sleep for 4 hours.
• Alternative Hypothesis (H1): Individuals who sleep for 8 hours will have better memory retention than those who sleep for 4 hours.
• Directional Hypothesis: Longer sleep duration will improve memory retention.
• If-Then Statement: If individuals sleep for 8 hours, then their memory retention will improve compared to those who sleep for 4 hours.

3. Education

Do interactive teaching methods improve student engagement?

• Null Hypothesis (H0): There is no significant difference in student engagement between interactive teaching methods and traditional lecture-based methods.
• Alternative Hypothesis (H1): Interactive teaching methods result in higher student engagement compared to traditional lecture-based methods.
• Directional Hypothesis: Interactive teaching methods will increase student engagement.
• If-Then Statement: If teachers use interactive teaching methods, then student engagement will increase.

4. Medicine

Does a new drug reduce blood pressure more effectively than the standard medication?

• Null Hypothesis (H0): There is no significant difference in blood pressure reduction between the new drug and the standard medication.
• Alternative Hypothesis (H1): The new drug reduces blood pressure more effectively than the standard medication.
• Directional Hypothesis: The new drug will reduce blood pressure more than the standard medication.
• If-Then Statement: If patients take the new drug, then their blood pressure will decrease more than if they take the standard medication.

5. Sociology

Does socioeconomic status affect access to higher education?

• Null Hypothesis (H0): There is no significant relationship between socioeconomic status and access to higher education.
• Alternative Hypothesis (H1): Higher socioeconomic status is associated with greater access to higher education.
• Directional Hypothesis: Individuals with higher socioeconomic status will have greater access to higher education.
• If-Then Statement: If individuals have a higher socioeconomic status, then they will have greater access to higher education.

Hypothesis Examples in Psychology

Psychology research often explores the relationships between various cognitive, behavioral, and emotional variables. Here are some well-structured hypothesis examples in psychology:

1. Sleep Duration and Memory Retention

• Non-Directional Hypothesis: There is a relationship between sleep duration and memory retention.

2. Exercise and Anxiety Levels

Does regular exercise reduce anxiety levels?

• Null Hypothesis (H0): There is no significant difference in anxiety levels between individuals who exercise regularly and those who do not.
• Alternative Hypothesis (H1): Individuals who exercise regularly will have lower anxiety levels than those who do not.
• Directional Hypothesis: Regular exercise will decrease anxiety levels.
• Non-Directional Hypothesis: There is a relationship between regular exercise and anxiety levels.
• If-Then Statement: If individuals exercise regularly, then their anxiety levels will decrease.

3. Social Media Usage and Self-Esteem

Does social media usage affect self-esteem in teenagers?

• Null Hypothesis (H0): There is no significant relationship between social media usage and self-esteem in teenagers.
• Alternative Hypothesis (H1): High social media usage is associated with lower self-esteem in teenagers.
• Directional Hypothesis: Increased social media usage will decrease self-esteem in teenagers.
• Non-Directional Hypothesis: There is a relationship between social media usage and self-esteem in teenagers.
• If-Then Statement: If teenagers spend more time on social media, then their self-esteem will decrease.

4. Cognitive Behavioral Therapy (CBT) and Depression

Is Cognitive Behavioral Therapy (CBT) effective in reducing symptoms of depression?

• Null Hypothesis (H0): There is no significant difference in depression symptoms between individuals who undergo CBT and those who do not.
• Alternative Hypothesis (H1): Individuals who undergo CBT will experience a greater reduction in depression symptoms than those who do not.
• Directional Hypothesis: CBT will reduce symptoms of depression.
• Non-Directional Hypothesis: There is a relationship between undergoing CBT and reduction in depression symptoms.
• If-Then Statement: If individuals undergo CBT, then their symptoms of depression will decrease.

5. Parental Involvement and Academic Achievement

Does parental involvement influence academic achievement in children?

• Null Hypothesis (H0): There is no significant relationship between parental involvement and academic achievement in children.
• Alternative Hypothesis (H1): Higher levels of parental involvement are associated with higher academic achievement in children.
• Directional Hypothesis: Increased parental involvement will improve academic achievement in children.
• Non-Directional Hypothesis: There is a relationship between parental involvement and academic achievement in children.
• If-Then Statement: If parents are more involved in their children’s education, then their children will achieve higher academic success.

Hypothesis Examples in Science

Scientific research often involves creating hypotheses to test the relationships between variables. Here are some well-structured hypothesis examples from various fields of science:

1. Biology: Sunlight and Plant Growth

• Non-Directional Hypothesis: There is a relationship between sunlight exposure and plant growth.

2. Chemistry: Temperature and Reaction Rate

Does temperature affect the rate of a chemical reaction?

• Null Hypothesis (H0): There is no significant difference in the reaction rate of a chemical reaction at different temperatures.
• Alternative Hypothesis (H1): Increasing the temperature will increase the reaction rate.
• Directional Hypothesis: Higher temperatures will increase the reaction rate.
• Non-Directional Hypothesis: There is a relationship between temperature and the reaction rate.
• If-Then Statement: If the temperature of a reaction increases, then the reaction rate will increase.

3. Physics: Mass and Free Fall Speed

Does the mass of an object affect its speed when falling?

• Null Hypothesis (H0): There is no significant difference in the falling speed of objects with different masses.
• Alternative Hypothesis (H1): Objects with greater mass fall faster than those with lesser mass.
• Directional Hypothesis: Heavier objects will fall faster than lighter objects.
• Non-Directional Hypothesis: There is a relationship between the mass of an object and its falling speed.
• If-Then Statement: If an object’s mass increases, then its falling speed will increase.

4. Environmental Science: Fertilizers and Water Quality

Do chemical fertilizers affect water quality in nearby lakes?

• Null Hypothesis (H0): There is no significant effect of chemical fertilizers on the water quality of nearby lakes.
• Alternative Hypothesis (H1): Chemical fertilizers negatively affect the water quality of nearby lakes.
• Directional Hypothesis: The use of chemical fertilizers will decrease the water quality of nearby lakes.
• Non-Directional Hypothesis: There is a relationship between the use of chemical fertilizers and the water quality of nearby lakes.
• If-Then Statement: If chemical fertilizers are used, then the water quality in nearby lakes will decrease.

5. Earth Science: Soil Composition and Erosion Rate

Does soil composition affect the rate of erosion?

• Null Hypothesis (H0): There is no significant difference in the erosion rate of soils with different compositions.
• Alternative Hypothesis (H1): Soil composition affects the rate of erosion.
• Directional Hypothesis: Soils with higher clay content will erode more slowly than sandy soils.
• Non-Directional Hypothesis: There is a relationship between soil composition and the rate of erosion.
• If-Then Statement: If soil has a higher clay content, then its erosion rate will be lower compared to sandy soil.

Hypothesis Examples in Biology

In biology, hypotheses are used to explore relationships and effects within biological systems. Here are some well-structured hypothesis examples in various areas of biology:

1. Photosynthesis and Light Intensity

How does light intensity affect the rate of photosynthesis in plants?

• Null Hypothesis (H0): Light intensity has no significant effect on the rate of photosynthesis in plants.
• Alternative Hypothesis (H1): Light intensity significantly affects the rate of photosynthesis in plants.
• Directional Hypothesis: Increased light intensity will increase the rate of photosynthesis in plants.
• Non-Directional Hypothesis: There is a relationship between light intensity and the rate of photosynthesis in plants.
• If-Then Statement: If light intensity increases, then the rate of photosynthesis in plants will increase.

2. Temperature and Enzyme Activity

How does temperature affect the activity of the enzyme amylase?

• Null Hypothesis (H0): Temperature has no significant effect on the activity of the enzyme amylase.
• Alternative Hypothesis (H1): Temperature significantly affects the activity of the enzyme amylase.
• Directional Hypothesis: Increasing the temperature will increase the activity of the enzyme amylase up to an optimal point, after which activity will decrease.
• Non-Directional Hypothesis: There is a relationship between temperature and the activity of the enzyme amylase.
• If-Then Statement: If the temperature increases, then the activity of the enzyme amylase will increase up to an optimal temperature, after which it will decrease.

3. Nutrient Availability and Plant Growth

Does the availability of nutrients in soil affect the growth of plants?

• Null Hypothesis (H0): Nutrient availability has no significant effect on the growth of plants.
• Alternative Hypothesis (H1): Nutrient availability significantly affects the growth of plants.
• Directional Hypothesis: Increased nutrient availability will enhance plant growth.
• Non-Directional Hypothesis: There is a relationship between nutrient availability and plant growth.
• If-Then Statement: If nutrient availability in the soil increases, then the growth of plants will be enhanced.

4. Genetic Variation and Disease Resistance

Does genetic variation in a population affect its resistance to diseases?

• Null Hypothesis (H0): Genetic variation has no significant effect on disease resistance in a population.
• Alternative Hypothesis (H1): Genetic variation significantly affects disease resistance in a population.
• Directional Hypothesis: Populations with greater genetic variation will have higher resistance to diseases.
• Non-Directional Hypothesis: There is a relationship between genetic variation and disease resistance in a population.
• If-Then Statement: If a population has greater genetic variation, then its resistance to diseases will be higher.

5. Water pH and Aquatic Life Health

Does the pH level of water affect the health of aquatic life?

• Null Hypothesis (H0): The pH level of water has no significant effect on the health of aquatic life.
• Alternative Hypothesis (H1): The pH level of water significantly affects the health of aquatic life.
• Directional Hypothesis: Extreme pH levels (both high and low) will negatively affect the health of aquatic life.
• Non-Directional Hypothesis: There is a relationship between the pH level of water and the health of aquatic life.
• If-Then Statement: If the pH level of water is too high or too low, then the health of aquatic life will be negatively affected.

Hypothesis Examples in Sociology

In sociology, hypotheses are used to explore and explain social phenomena, behaviors, and relationships within societies. Here are some well-structured hypothesis examples in various areas of sociology:

1. Education and Social Mobility

Does access to higher education affect social mobility?

• Null Hypothesis (H0): Access to higher education has no significant effect on social mobility.
• Alternative Hypothesis (H1): Access to higher education significantly affects social mobility.
• Directional Hypothesis: Increased access to higher education will improve social mobility.
• Non-Directional Hypothesis: There is a relationship between access to higher education and social mobility.
• If-Then Statement: If individuals have increased access to higher education, then their social mobility will improve.

2. Income Inequality and Crime Rates

Does income inequality influence crime rates in urban areas?

• Null Hypothesis (H0): Income inequality has no significant effect on crime rates in urban areas.
• Alternative Hypothesis (H1): Income inequality significantly affects crime rates in urban areas.
• Directional Hypothesis: Higher income inequality will lead to higher crime rates in urban areas.
• Non-Directional Hypothesis: There is a relationship between income inequality and crime rates in urban areas.
• If-Then Statement: If income inequality increases, then crime rates in urban areas will increase.

3. Social Media Use and Social Interaction

Does the use of social media affect face-to-face social interactions among teenagers?

• Null Hypothesis (H0): The use of social media has no significant effect on face-to-face social interactions among teenagers.
• Alternative Hypothesis (H1): The use of social media significantly affects face-to-face social interactions among teenagers.
• Directional Hypothesis: Increased use of social media will decrease face-to-face social interactions among teenagers.
• Non-Directional Hypothesis: There is a relationship between the use of social media and face-to-face social interactions among teenagers.
• If-Then Statement: If teenagers use social media more frequently, then their face-to-face social interactions will decrease.

4. Gender Roles and Career Choices

Do traditional gender roles influence career choices among young adults?

• Null Hypothesis (H0): Traditional gender roles have no significant effect on career choices among young adults.
• Alternative Hypothesis (H1): Traditional gender roles significantly affect career choices among young adults.
• Directional Hypothesis: Adherence to traditional gender roles will limit career choices among young adults.
• Non-Directional Hypothesis: There is a relationship between traditional gender roles and career choices among young adults.
• If-Then Statement: If young adults adhere to traditional gender roles, then their career choices will be limited.

5. Cultural Diversity and Workplace Productivity

Does cultural diversity in the workplace affect productivity levels?

• Null Hypothesis (H0): Cultural diversity in the workplace has no significant effect on productivity levels.
• Alternative Hypothesis (H1): Cultural diversity in the workplace significantly affects productivity levels.
• Directional Hypothesis: Increased cultural diversity will improve productivity levels in the workplace.
• Non-Directional Hypothesis: There is a relationship between cultural diversity in the workplace and productivity levels.
• If-Then Statement: If the workplace has increased cultural diversity, then productivity levels will improve.

More Hypothesis Samples & Examples in PDF

1. research hypothesis.

2. Education Hypothesis

3. Basic Hypothesis

4. Hypothesis Statement Template

5. Hypothesis in PDF

6. Hypothesis Format

7. Hypothesis Examples

8. Simple Hypothesis

Types of Hypothesis

A hypothesis is a statement that can be tested and is often used in scientific research to propose a relationship between two or more variables. Understanding the different types of hypotheses is essential for conducting effective research. Below are the main types of hypotheses:

1. Null Hypothesis (H0)

The null hypothesis states that there is no relationship between the variables being studied. It assumes that any observed effect is due to chance. Researchers often aim to disprove the null hypothesis.

Example: There is no significant difference in test scores between students who study with music and those who study in silence.

2. Alternative Hypothesis (H1 or Ha)

The alternative hypothesis suggests that there is a relationship between the variables being studied. It is what researchers seek to prove.

Example: Students who study with music have higher test scores than those who study in silence.

3. Simple Hypothesis

A simple hypothesis predicts a relationship between a single independent variable and a single dependent variable.

Example: Increasing the amount of sunlight will increase the growth rate of plants.

4. Complex Hypothesis

A complex hypothesis predicts a relationship involving two or more independent variables and/or two or more dependent variables.

Example: Increasing sunlight and water will increase the growth rate and height of plants.

5. Directional Hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. It suggests whether the relationship is positive or negative.

Example: Students who study for more hours will score higher on exams.

6. Non-Directional Hypothesis

A non-directional hypothesis does not specify the direction of the relationship. It only states that a relationship exists.

Example: There is a difference in test scores between students who study with music and those who study in silence.

7. Statistical Hypothesis

A statistical hypothesis involves quantitative data and can be tested using statistical methods. It often includes both null and alternative hypotheses.

Example: The mean test scores of students who study with music are significantly different from those who study in silence.

8. Causal Hypothesis

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

Example: Smoking causes lung cancer.

9. Associative Hypothesis

An associative hypothesis suggests that variables are related but does not imply causation.

Example: There is an association between physical activity levels and body weight.

10. Research Hypothesis

A research hypothesis is a broad statement that serves as the foundation for the research study. It is often the same as the alternative hypothesis.

Example: Implementing a new teaching strategy will improve student engagement and performance.

How To Use Hypothesis for Research?

A hypothesis is a critical component of the research process, providing a clear direction for the study and forming the basis for drawing conclusions. Here’s a step-by-step guide on how to use a hypothesis in research:

1. Identify the Research Problem

Before formulating a hypothesis, clearly define the research problem or question. This step involves understanding what you aim to investigate and why it is significant.

Example: You want to study the impact of sleep on academic performance among college students.

2. Review Existing Literature

Conduct a thorough review of existing literature to understand what is already known about the topic. This helps in identifying gaps in knowledge and forming a basis for your hypothesis.

Example: Previous studies suggest a positive correlation between sleep duration and academic performance but lack specific data on college students.

Based on the research problem and literature review, formulate a clear and testable hypothesis. Ensure it is specific and relates directly to the variables being studied.

Types of Hypotheses:

• Null Hypothesis (H0): There is no significant relationship between sleep duration and academic performance among college students.
• Alternative Hypothesis (H1): There is a significant relationship between sleep duration and academic performance among college students.

4. Define Variables

Clearly define the independent and dependent variables involved in the hypothesis.

• Independent Variable: Sleep duration
• Dependent Variable: Academic performance (e.g., GPA)

5. Design the Study

Choose an appropriate research design to test the hypothesis. This could be experimental, correlational, or observational, depending on the nature of your research question.

Example: Conduct a correlational study to examine the relationship between sleep duration and GPA among college students.

6. Collect Data

Gather data through surveys, experiments, or secondary data sources. Ensure the data collection methods are reliable and valid to accurately test the hypothesis.

Example: Use a questionnaire to collect data on students’ sleep duration and their GPAs.

7. Analyze the Data

Use appropriate statistical methods to analyze the data. This step involves testing the hypothesis to determine whether to accept or reject the null hypothesis.

Example: Perform a Pearson correlation analysis to examine the relationship between sleep duration and GPA.

8. Interpret the Results

Interpret the results of the statistical analysis. Determine if the data supports the alternative hypothesis or if the null hypothesis cannot be rejected.

Example: If the analysis shows a significant positive correlation, you can reject the null hypothesis and accept the alternative hypothesis that sleep duration is related to academic performance.

9. Draw Conclusions

Draw conclusions based on the results of the hypothesis testing. Discuss the implications of the findings and how they contribute to the existing body of knowledge.

Example: Conclude that longer sleep duration is associated with higher GPA among college students and discuss potential implications for student health and academic policies.

10. Report and Share Findings

Write a detailed report or research paper presenting the hypothesis, methodology, results, and conclusions. Share your findings with the academic community or relevant stakeholders.

Example: Publish the study in a peer-reviewed journal or present it at an academic conference.

How to Write a Hypothesis?

Writing a hypothesis is a crucial step in the scientific method. A well-constructed hypothesis guides your research, helping you design experiments and analyze results. Here’s a step-by-step guide on how to write an effective hypothesis:

1. Understand the Research Question

Start by clearly understanding the research question or problem you want to address. This helps in formulating a focused hypothesis.

Example: How does sunlight exposure affect plant growth?

2. Conduct Preliminary Research

Review existing literature related to your research question. This helps in understanding what is already known and identifying gaps in knowledge.

Example: Studies show that plants generally grow better with more sunlight, but the optimal amount varies.

3. Identify Variables

Determine the independent and dependent variables for your study.

• Independent Variable: The factor you manipulate (e.g., sunlight exposure).
• Dependent Variable: The factor you measure (e.g., plant growth).

4. Formulate a Simple Hypothesis

A simple hypothesis involves one independent and one dependent variable. Clearly state the expected relationship between these variables.

Example: Increasing sunlight exposure will increase plant growth.

5. Choose the Type of Hypothesis

Decide whether your hypothesis will be null or alternative, directional or non-directional.

• Null Hypothesis (H0): There is no relationship between the variables.
• Alternative Hypothesis (H1): There is a relationship between the variables.
• Directional Hypothesis: Specifies the direction of the relationship.
• Non-Directional Hypothesis: Does not specify the direction.

Example of Directional Hypothesis: Plants exposed to more sunlight will grow taller than those exposed to less sunlight.

6. Ensure Testability

Make sure your hypothesis can be tested through experiments or observations. It should be measurable and falsifiable.

Example: Plants will be grown under different levels of sunlight, and their growth will be measured over time.

7. Write the Hypothesis

Write your hypothesis in a clear, concise, and specific manner. It should include the variables and the expected relationship between them.

Example: If plants are exposed to increased sunlight, then they will grow taller compared to plants that receive less sunlight.

8. Refine the Hypothesis

Ensure that your hypothesis is specific and narrow enough to be testable but broad enough to cover the scope of your research.

Example: If tomato plants are exposed to 8 hours of sunlight per day, then they will grow taller and produce more fruit compared to tomato plants exposed to 4 hours of sunlight per day.

How Do You Formulate a Hypothesis?

To formulate a hypothesis, identify the research question, review existing literature, define variables, and create a testable statement predicting the relationship between the variables.

What Is the Difference Between Null and Alternative Hypotheses?

The null hypothesis (H0) states there is no effect or relationship, while the alternative hypothesis (H1) proposes that there is an effect or relationship.

Why Is a Hypothesis Important in Research?

A hypothesis provides a clear focus for the study, guiding the research design, data collection, and analysis, ultimately helping to draw meaningful conclusions.

Can a Hypothesis Be Proven True?

A hypothesis cannot be proven true; it can only be supported or refuted through experimentation and analysis. Even if supported, it remains open to further testing.

What Makes a Good Hypothesis?

A good hypothesis is clear, concise, specific, testable, and based on existing knowledge. It should predict a relationship between variables that can be measured.

How Is a Hypothesis Tested?

A hypothesis is tested through experiments or observations, collecting and analyzing data to determine if the results support or refute the hypothesis.

What Are the Types of Hypotheses?

Types of hypotheses include null, alternative, simple, complex, directional, non-directional, statistical, causal, and associative.

What Is a Directional Hypothesis?

A directional hypothesis specifies the expected direction of the relationship between variables, indicating whether the effect will be positive or negative.

What Is a Non-Directional Hypothesis?

A non-directional hypothesis states that a relationship exists between variables but does not specify the direction of the relationship.

How Do You Refine a Hypothesis?

Refine a hypothesis by ensuring it is specific, measurable, and testable. Remove any vague terms and focus on a single independent and dependent variable.

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Workshop titled “First Steps in Research: Research Paper Writing for Beginners”

On 24th May 2024, AIUB Computer Club organized a workshop named “First Steps in Research: Research Paper Writing for Beginners” at 8:00 PM via Microsoft Teams. The workshop was conducted by Md. Saef Ullah Miah ( Assistant Professor and Program Assessor at IQAC-AIUB ).  He is also a former president of the AIUB computer club.

Mr. Mirza Saikat Ahmmed (Assistant General Secretary, AIUB Computer Club) extended a warm welcome to Mr. Miah to commence the workshop.  The primary objective of the workshop was to guide beginners through the process of writing a research paper and to impart knowledge on the key aspects of effective research writing.  Mr. Miah initiated the session by expressing his gratitude to the attendees and proceeded to provide an overview of the research process, defining the concept of research. In easy words, he explained that research is discovering new information and how impactful it can be for society. The speaker outlined the general categories of research and provided a brief overview of each type of research paper, emphasizing the importance of having one’s work reviewed by a community of distinguished individuals actively engaged in research. He then addressed that “peer review” is very important in writing a research paper and talked about the importance of writing a research paper which is to apply for a master's degree abroad. Furthermore, Mr. Miah underscored the critical role of ‘peer review’ in the research paper writing process. He also discussed the relevance of publishing research papers, particularly for those aspiring to pursue a master’s degree abroad. He also stated that publishing research is crucial for gaining experience as a researcher and for achieving recognition within the academic community.

Mr. Miah did a quick review of the workshop to assess the understanding of the participants by taking a short quiz. Following the recap quiz, he discussed five methods for writing a research paper, with a particular focus on conference paper writing. He explained that conference paper writing is often more approachable than journal writing, making it a suitable starting point for beginners interested in research. He then outlined the common elements of a research paper, including the Title, Abstract, Introduction, Literature Review, Methodology, Results, Discussion, Conclusion, and References.

The speaker also provided guidance on the step-by-step process of writing a conference paper. Through a Google Form, participants were able to gain individual insights into each step of writing a conference paper. Mr. Miah facilitated an interactive session, assisting participants as they selected their topics. The next step involved searching for information using tools such as Google Scholar, IEEE Xplore, ACM Digital Library, and Elsevier. He demonstrated the use of these tools with practical examples, showing how they make information readily accessible.

Mr. Miah further discussed how journals’ online pages are available in two formats: open-access and paywall. He noted that conference papers are often behind paywalls. He then demonstrated how to locate desired research papers using Google Scholar, Research Rabbit, and ResearchGate, and guided participants on discovering recent research papers, suggesting that Research Rabbit is often more relevant than Google Scholar. He introduced tools for research paper writing, such as Elicit, Research Rabbit, and Google Scholar, and described the step-by-step process of writing a conference paper, starting with formulating a research question or hypothesis and then designing the methodology. He covered data finding and primary data collection, continuing the interactive session by explaining each step in detail.

In the meantime,  Mr. Abhijit Bhowmik  ( Special Assistant for the Office of Student Affairs and Associate Professor of Computer Science at AIUB ) joined the workshop expressed his appreciation to Mr. Miah for conducting such an informative session for the students. He also acknowledged Mr. Miah’s past contributions as the founder and president of the AIUB Computer Club. Mr. Bhowmik then motivated and encouraged those who were extremely interested in doing research work in the future. The speaker mentioned that there will be a detailed workshop in the future on tools for research work. He discussed the use of Microsoft Word, the most popular tool for writing papers, and emphasized the importance of revising work with online tools like Grammarly. He advised seeking feedback from peers and professional researchers and provided overall feedback on the conference paper submission process.

The honorable speaker , Md. Saef Ullah Miah wrapped up the workshop with a beneficial Q&A session. Mr. Ahmmed then took the floor to express gratitude to the speaker and thanked everyone for attending the informative workshop. The workshop was open to all the students. Around one hundred and ninety people joined the workshop and showed their enthusiasm. The workshop proved to be highly informative, covering the fundamental steps of writing a research paper for beginners.

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What are the Market Research Methods?

• August 15, 2023
• Topic: Brand Strategy , Corporate , Corporate Trends , Customer Experience , Market Analysis , Product Lifecycle
• Resource type: Insights Blog

So, you have a question, but you are unsure of how to get your answer. Maybe you are wondering who your target audience is or why you lost out on a deal to your competition. Maybe you are looking to expand into a new market and want to know more about the customers and competitors in the industry. While these examples are similar in the way they help you understand your business better, they all require different market research methodologies to arrive at the answer.  

What are Market Research Methodologies?  

Research methodologies are various ways to perform research to understand your problem. The correct type to employ depends on the answers you are seeking, the information you have, and the information you need to gather. There are many different methods, but most fall into four categories: data analytics, survey, qualitative, and secondary.

In this post, we will provide an overview of the four main research methodologies along with the benefits and challenges of each.  

Custom or Syndicated Research  

In addition to the types of methodologies, there are two types of funded research: custom and syndicated.   

Custom research is funded by a single company and is focused on answering the key questions the business seeks to understand. Though more costly, the research design, implementation, and results are unique and targeted toward addressing the funding company’s needs.  

On the other hand, syndicated research is not curated or funded by a specific client; a market research company conducts it to offer data such as industry statistics, current best practices, or recent trends. Though not directly tied to a single company’s situation, businesses often buy syndicated research to gather perspective on their performance and identify areas where custom research can help provide more insight.   

The Four Types Of Market Research   

Data analytics  .

Data analytics research involves collecting and analyzing large sets of data to derive answers, uncover patterns, and predict future outcomes. This method helps you identify and understand things you are aware of but don’t yet understand.

Data can come from a variety of sources including CRM data, historical transactional data, survey data, a third-party publisher, and more to build a holistic map of the situation, identify gaps and discern trends. Data analytics is the most common research method with almost 70% of companies using it in at least one market research project in the past year.  

For example, you might have large sets of historical data and know there is a data-backed answer for how to segment your customers, but you have yet to compile all your information together to identify the answer.  

Benefits and Challenges

Benefits: Analyzing historical data provides a holistic view of a situation by combining different sets of data and modeling potential scenarios and outcomes. You can confirm hypotheses, break biases, and help build cases internally.

Challenges: This method requires a lot of data, and some of that data may be hard to access, hard to generate, or not easily analyzed. This method also requires a lot of time, money, and resources to acquire and parse the correct data.   

Survey research involves gathering opinions, preferences, and experiences by asking a set of questions to a targeted group of people. The focus of survey research is to test theories, assumptions, and hypotheses. The answers are collected from a representative sample of a targeted audience, allowing the researchers to quantify data and generalize the results to the wider population with a reasonable margin of error and strong confidence level.

Survey data can be collected from consumers, other business decision-makers, or your customer lists. Surveys are a very popular market research methodology with over 60% of companies performing at least one survey in the last year.  

For example, you may be wondering how satisfied your customers are, what factors drive satisfaction, and how you compare to key competitors in the market. By surveying your customers and those of key competitors you can understand the drivers of satisfaction and your relative strengths and opportunity areas in the market.  

Benefits and Challenges 

Benefits : Surveys provide an aggregate but statistically significant picture that companies can leverage to make decisions that align with their audiences’ preferences. Surveys also offer the ability to segment answers based on segments of the audience to analyze how different groups respond to the same questions.   

Challenges: Surveys are a fixed set of questions and cannot be adjusted once the survey has been deployed. Responses are limited to the questions posed by the researcher and don’t allow for open-ended qualitative responses. Surveys require many respondents, and depending on the target audience, it can be challenging to find a large enough sample size to provide statistically significant results. Lastly, surveys need to incentivize respondents, which could lead to a high price tag.  

Qualitative Methodologies   

Qualitative research focuses on targeted insights around concepts, opinions, and preferences. Unlike quantitative methods, these market research methodologies leverage a smaller set of data and respondents but allow for more in-depth answers. It also allows for companies to gather follow-up data that delves deeper into the reasoning behind responses  

This method is exploratory in nature to help you formulate hypothesis and establish directional themes or trends. Qualitative research also helps you understand the underlying motivations, attitudes, and perceptions of respondents.  

 The two most common qualitative research methodologies are in-depth interviews and focus groups.  

In-depth interviews  

This market research methodology involves one-on-one conversations between interviewers and those from the target audience. The interview follows a pre-determined set of questions to reveal sentiment, decision-making processes, and unmet needs. With only 40% of companies conducting them, interviews are the least used methodology, likely a result of the challenges mentioned below.  

Benefits : Interviews provide the ability to gather more in-depth answers on customer preferences by allowing researchers to ask follow-up questions to probe deeper and further clarify responses. It also allows respondents to answer in their own words rather than be bound by the available responses offered by a survey. 

Challenges: Interviews are responses from a small group of people and the results cannot be generalized to a wider audience. They are also very challenging to implement. Often, it is a struggle to identify and incentivize enough participants, and the price per respondent can be costly depending on their rarity and level of expertise. It is also critical to enlist an experienced interviewer to ensure that both the initial and follow-up questions are tailored to gather accurate information that fully addresses your target questions.   

  Focus groups   

These facilitator-led group discussions reveal perceptions of or reception to a concept or idea. While the facilitator guides the meeting, the direction of the conversation is determined by the participants creating organic responses that stem from participant perception. Just over half of companies have conducted a focus group in the last year.   

Benefits: F ocus groups allow for exploration of concepts and physical products beyond set responses like those available in through a survey. The social aspect of the focus group can also gather multiple points of view on a topic in one setting. This can add additional insight for both participants in their ongoing feedback and facilitators for their final analysis.

Challenges: Focus groups are kept small to gather meaningful insights from a group of people, something that would be difficult if the group was too large. As such, the sample size is very small, and the responses can‘t be extrapolated to a larger audience.  It is also challenging to find a group of qualified participants that are all available at the same time.

Traditionally, focus groups were conducted in person and there was a higher cost to host the group live. Now depending on the product or concept being reviewed, focus groups can be conducted over video calls, lessening the burden of cost and logistics, however the cost to incentivize members to take part remains. Similar to interviews, you will need to enlist an experienced moderator that can facilitate the conversation and help direct it as needed to ensure the target questions are addressed  

Secondary Research Methodologies     

Secondary research, a lso known as desk research, is leveraging data that already exists to answer your questions. This market research method is helpful for answering questions or deepening your understanding of things you are not directly familiar with but understand. It can be used to understand what others in your market are doing, identify potential markets for growth or expansion, or allow you to compare your organization to others on key performance indicators.  

For example, you might understand that customer preferences have affected your market, but you don’t know the exact changes. However, others have already done related research that can provide context or direct answers to your question. Secondary research is a very popular method with over half ( 55% ) of companies conducting secondary research to get insights they need for their strategies.   

Benefits: Secondary research is one of the quicker methodologies as it leverages existing data. The bulk of the time is spent identifying the problem, accessing existing data, and consolidating it for analysis and insights.   

Challenges: Some of the data you need might require payment, which would increase the cost of the overall project. There is also the risk that a data point needed for your analysis does not exist, requiring you to either speak to an expert or conduct your own research to fill in the gap.  

Picking the Right Research Methodology  

Though there are many options to choose from, the correct market research methodology to implement will be guided by the information you already have and the questions you are trying to answer.

Before you start your research, begin by listing what you know and what you are looking to learn. Some choices are very clear cut. For example, are you looking to learn more about your company’s operations in the hopes of identifying a better strategy? Since you have access to your own data and are looking to learn more, data analytics would be your best path forward.  

Sometimes choosing the right market research methodology might require more thought. For example, are you looking to launch a new product and want to learn more about customer preferences? You could interview customers or launch a focus group, but do you know what questions to ask? And as the sample pool is so small, the results from qualitative methods should not be used to make assumptions about a larger customer base.

The best place to start would be to conduct a survey to the target audience to get a basic understanding of the market and potential customer preferences. If it is a well-known customer base, you may be able to through secondary research by leveraging existing data to analyze the market.  

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• Formulation of Hypothesis

Children who spend more time playing outside are more likely to be imaginative. What do you think this statement is an example of in terms of scientific research ? If you guessed a hypothesis, then you'd be correct. The formulation of hypotheses is a fundamental step in psychology research.

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• First, we will discuss the importance of hypotheses in research.
• We will then cover formulating hypotheses in research, including the steps in the formulation of hypotheses in research methodology.
• We will provide examples of hypotheses in research throughout the explanation.
• Finally, we will delve into the different types of hypotheses in research.

What is a Hypothesis?

The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research . To be classed as scientific research , it must be observable, valid, reliable and follow a standardised procedure.

One of the important steps in scientific research is to formulate a hypothesis before starting the study procedure.

The hypothesis is a predictive, testable statement predicting the outcome and the results the researcher expects to find.

The hypothesis provides a summary of what direction, if any, is taken to investigate a theory.

In scientific research, there is a criterion that hypotheses need to be met to be regarded as acceptable.

If a hypothesis is disregarded, the research may be rejected by the community of psychology researchers.

Importance of Hypothesis in Research

The purpose of including hypotheses in psychology research is:

• To provide a summary of the research, how it will be investigated, and what is expected to be found.
• To provide an answer to the research question.

When carrying out research, researchers first investigate the research area they are interested in. From this, researchers are required to identify a gap in the literature.

Filling the gap essentially means finding what previous work has not been explained yet, investigated to a sufficient degree, or simply expanding or further investigating a theory if doubt exists.

The researcher then forms a research question that the researcher will attempt to answer in their study.

Remember, the hypothesis is a predictive statement of what is expected to happen when testing the research question.

The hypothesis can be used for later data analysis. This includes inferential tests such as hypothesis testing and identifying if statistical findings are significant.

Steps in the Formulation of Hypothesis in Research Methodology

Researchers must follow certain steps to formulate testable hypotheses when conducting research.

Overall, the researcher has to consider the direction of the research, i.e. will it be looking for a difference caused by independent variables ? Or will it be more concerned with the correlation between variables?

All researchers will likely complete the following.

• Investigating background research in the area of interest.
• Formulating or investigating a theory.
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works.

The above steps are used to formulate testable hypotheses.

The Formulation of Testable Hypotheses

The hypothesis is important in research as it indicates what and how a variable will be investigated.

The hypothesis essentially summarises what and how something will be investigated. This is important as it ensures that the researcher has carefully planned how the research will be done, as the researchers have to follow a set procedure to conduct research.

This is known as the scientific method.

Formulating Hypotheses in Research

When formulating hypotheses, things that researchers should consider are:

Types of Hypotheses in Research

Researchers can propose different types of hypotheses when carrying out research.

The following research scenario will be discussed to show examples of each type of hypothesis that the researchers could use. "A research team was investigating whether memory performance is affected by depression ."

The identified independent variable is the severity of depression scores, and the dependent variable is the scores from a memory performance task.

The null hypothesis predicts that the results will show no or little effect. The null hypothesis is a predictive statement that researchers use when it is thought that the IV will not influence the DV.

In this case, the null hypothesis would be there will be no difference in memory scores on the MMSE test of those who are diagnosed with depression and those who are not.

An alternative hypothesis is a predictive statement used when it is thought that the IV will influence the DV. The alternative hypothesis is also called a non-directional, two-tailed hypothesis, as it predicts the results can go either way, e.g. increase or decrease.

The example in this scenario is there will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

The directional alternative hypothesis states how the IV will influence the DV, identifying a specific direction, such as if there will be an increase or decrease in the observed results.

The example in this scenario is people with low depressive scores will perform better in the memory performance task than people who score higher in depressive symptoms.

Example Hypothesis in Research

To summarise, let's look at an example of a straightforward hypothesis that indicates the relationship between two variables: the independent and the dependent.

If you stay up late, you will feel tired the following day; the more caffeine you drink, the harder you find it to fall asleep, or the more sunlight plants get, the taller they will grow.

Formulation of Hypothesis - Key Takeaways

• The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research. One of the important steps in scientific research is to create a hypothesis.
• The hypothesis is a predictive, testable statement concerning the outcome/results that the researcher expects to find.
• Hypotheses are needed in research to provide a summary of what the research is, how to investigate a theory and what is expected to be found, and to provide an answer to the research question so that the hypothesis can be used for later data analysis.
• There are requirements for the formulation of testable hypotheses. The hypotheses should identify and operationalise the IV and DV. In addition, they should describe the nature of the relationship between the IV and DV.
• There are different types of hypotheses: Null hypothesis, Alternative hypothesis (this is also known as the non-directional, two-tailed hypothesis), and Directional hypothesis (this is also known as the one-tailed hypothesis).

Flashcards in Formulation of Hypothesis 18

What type of hypothesis matches the following definition. A predictive statement that researchers use when it is thought that the IV will not influence the DV.

Null hypothesis 

What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV. But, the hypothesis does not state how the IV will influence the DV. 

Alternative hypothesis 

What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV, and states how it will influence the DV. 

Directional, alternative hypothesis 

Which type of hypothesis is also known as a two-tailed hypothesis? 

What type of hypothesis is the following example. There will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

What type of hypothesis is the following example. There will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

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Frequently Asked Questions about Formulation of Hypothesis

What are the 3 types of hypotheses?

The three types of hypotheses are:

• Null hypothesis 
• Alternative hypothesis 
• Directional/non-directional hypothesis 

What is an example of a hypothesis in psychology?

An example of a null hypothesis in psychology is, there will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

What are the steps in formulating a hypothesis?

All researchers will likely complete the following

• Investigating background research in the area of interest 
• Formulating or investigating a theory 
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works 

What is formulation of hypothesis in research? 

The formulation of a hypothesis in research is when the researcher formulates a predictive statement of what is expected to happen when testing the research question based on background research.

How to formulate  null and alternative hypothesis?

When formulating a null hypothesis the researcher would state a prediction that they expect to see no difference in the dependent variable when the independent variable changes or is manipulated. Whereas, when using an alternative hypothesis then it would be predicted that there will be a change in the dependent variable. The researcher can state in which direction they expect the results to go. 

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Color biomimetics in textile design: reproduction of natural plant colors through instrumental colorant formulation.

1. Introduction

2. materials and methods, 2.1. textile dyeing, 2.2. spectral reflectance, 2.3. instrumental colorant formulation, 2.3.1. spectrophotometric calibration data, 2.3.2. colorant formulation recipe, 2.4. qualitative and quantitative analysis, 3. results and discussion, 3.1. textile dyed concentration samples, 3.1.1. colorimetric properties of the concentration samples, 3.1.2. color strength (k/s), 3.2. biomimicry color of selected flower petals and leaves, 3.2.1. instrumental color recipe formulation, 3.2.2. visual analysis, 3.2.3. spectral reflectance analysis, 3.3. scanning electron microscope and optical microscopy analysis, 3.4. white light interferometry, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Cabral, I.; Schuch, A.; Steffens, F. Color Biomimetics in Textile Design: Reproduction of Natural Plant Colors through Instrumental Colorant Formulation. J. Imaging 2024 , 10 , 150. https://doi.org/10.3390/jimaging10070150

Cabral I, Schuch A, Steffens F. Color Biomimetics in Textile Design: Reproduction of Natural Plant Colors through Instrumental Colorant Formulation. Journal of Imaging . 2024; 10(7):150. https://doi.org/10.3390/jimaging10070150

Cabral, Isabel, Amanda Schuch, and Fernanda Steffens. 2024. "Color Biomimetics in Textile Design: Reproduction of Natural Plant Colors through Instrumental Colorant Formulation" Journal of Imaging 10, no. 7: 150. https://doi.org/10.3390/jimaging10070150

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