where is the hypothesis located in a research article

Where to Find The Hypothesis in a Research Article

The question of “Where to Find The Hypothesis in a Research Article” can only be answered by exploring how research articles represent scientific methods.

Table of Contents

Introduction

A research article represents a compilation of information by a scientist concerning an original research idea. It is characterized by a wide range of information including, the purpose of the study, the thesis statement, hypothesis, literature review, methodology, results and conclusion.

The examination of a research article is an important process, and the ability to identify crucial elements of research is paramount for the effective analysis of a research article.

Research articles are usually arranged in specific ways. A hypothesis in a research article is usually located in a specific position in an article. The ability to quickly pinpoint where the hypothesis is located is crucial in becoming an expert in exploring research articles as well as formulating them.

What is a hypothesis

A hypothesis represents a scientific guess that is stated in research. It is a speculative statement concerning the relationship between two or more variables in research.

Therefore, a good hypothesis is a prediction that is testable, specific, and explores what a researcher expects to find in the study.

Formulating a Hypothesis

The creation of a hypothesis represents a critical part of the scientific method. Formulating a hypothesis is important, especially when testing a theory. Most scientific research involves testing theories. Theories, in this case, refer to ideas about the way things relate to one another. For one to formulate a hypothesis to be used in research, they have to be to predict the outcome of the research.

If one cannot predict the outcome, then the research does not need the formulating of a hypothesis because it is either exploratory or descriptive. These forms of research cannot have a hypothesis, and the reason is that there is a limited base of knowledge concerning the subject matter for the prediction of the outcome to be possible.

A good Hypothesis

A good hypothesis has to have two or more variables. These variables have to be measurable or have the potential to be measured. The hypothesis also has to specify how the variables are related to one another.

Where to Find the Hypothesis in a Research Article

The scientific method is characterized by several steps. They include:

• Coming up with a question or the problem that needs to be solved.
• Conducting background research on the problem
• Formulating a hypothesis
• Establishing how the research will be conducted using a research design
• Collecting data
• Analyzing the data and coming up with results
• Provide conclusions
• Presenting the information through a research article.

Based on the above structure, it is evident that the hypothesis is located in the introduction section of a research article. One should look out for “if-then” statements. However, for such statements to be hypotheses, they need to:

• Demonstrates the relationship between variables,
• The relationship needs to be testable and
• The prediction needs to be measurable

A hypothesis is not always clearly labeled. This means that the statement can appear in different forms apart from when formulated using the “if-then” statements. One should, therefore, look out for a statement that offers a prediction of what readers need to expect from the research.

The ability to identify where to find a hypothesis is located in a research article is very important in several ways:

• One can quickly know what the researcher wants to prove using the research.
• It makes individuals effective in reading research articles.
• It enhances an individual’s ability to formulate their own hypothesis when conducting research

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

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

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

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

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

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

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

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Step 1. Ask a question

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

Step 2. Do some preliminary research

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

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

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

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

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

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

 Statistics

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

Research bias

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

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

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

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

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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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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.

Need a helping hand?

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.

17 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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

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Step-by-Step Guide: How to Craft a Strong Research Hypothesis

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Table of Contents

A research hypothesis is a concise statement about the expected result of an experiment or project. In many ways, a research hypothesis represents the starting point for a scientific endeavor, as it establishes a tentative assumption that is eventually substantiated or falsified, ultimately improving our certainty about the subject investigated.   

To help you with this and ease the process, in this article, we discuss the purpose of research hypotheses and list the most essential qualities of a compelling hypothesis. Let’s find out!  

How to Craft a Research Hypothesis  

Crafting a research hypothesis begins with a comprehensive literature review to identify a knowledge gap in your field. Once you find a question or problem, come up with a possible answer or explanation, which becomes your hypothesis. Now think about the specific methods of experimentation that can prove or disprove the hypothesis, which ultimately lead to the results of the study.   

Enlisted below are some standard formats in which you can formulate a hypothesis¹ :  

• A hypothesis can use the if/then format when it seeks to explore the correlation between two variables in a study primarily.  

Example: If administered drug X, then patients will experience reduced fatigue from cancer treatment.  

• A hypothesis can adopt when X/then Y format when it primarily aims to expose a connection between two variables  

Example: When workers spend a significant portion of their waking hours in sedentary work , then they experience a greater frequency of digestive problems.  

• A hypothesis can also take the form of a direct statement.  

Example: Drug X and drug Y reduce the risk of cognitive decline through the same chemical pathways  

What are the Features of an Effective Hypothesis?  

Hypotheses in research need to satisfy specific criteria to be considered scientifically rigorous. Here are the most notable qualities of a strong hypothesis:  

• Testability: Ensure the hypothesis allows you to work towards observable and testable results.  
• Brevity and objectivity: Present your hypothesis as a brief statement and avoid wordiness.  
• Clarity and Relevance: The hypothesis should reflect a clear idea of what we know and what we expect to find out about a phenomenon and address the significant knowledge gap relevant to a field of study.   

Understanding Null and Alternative Hypotheses in Research  

There are two types of hypotheses used commonly in research that aid statistical analyses. These are known as the null hypothesis and the alternative hypothesis . A null hypothesis is a statement assumed to be factual in the initial phase of the study.   

For example, if a researcher is testing the efficacy of a new drug, then the null hypothesis will posit that the drug has no benefits compared to an inactive control or placebo . Suppose the data collected through a drug trial leads a researcher to reject the null hypothesis. In that case, it is considered to substantiate the alternative hypothesis in the above example, that the new drug provides benefits compared to the placebo.  

Let’s take a closer look at the null hypothesis and alternative hypothesis with two more examples:  

Null Hypothesis:  

The rate of decline in the number of species in habitat X in the last year is the same as in the last 100 years when controlled for all factors except the recent wildfires.  

In the next experiment, the researcher will experimentally reject this null hypothesis in order to confirm the following alternative hypothesis :  

The rate of decline in the number of species in habitat X in the last year is different from the rate of decline in the last 100 years when controlled for all factors other than the recent wildfires.  

In the pair of null and alternative hypotheses stated above, a statistical comparison of the rate of species decline over a century and the preceding year will help the research experimentally test the null hypothesis, helping to draw scientifically valid conclusions about two factors—wildfires and species decline.   

We also recommend that researchers pay attention to contextual echoes and connections when writing research hypotheses. Research hypotheses are often closely linked to the introduction ² , such as the context of the study, and can similarly influence the reader’s judgment of the relevance and validity of the research hypothesis.  

Seasoned experts, such as professionals at Elsevier Language Services, guide authors on how to best embed a hypothesis within an article so that it communicates relevance and credibility. Contact us if you want help in ensuring readers find your hypothesis robust and unbiased.  

References  

• Hypotheses – The University Writing Center. (n.d.). https://writingcenter.tamu.edu/writing-speaking-guides/hypotheses  
• Shaping the research question and hypothesis. (n.d.). Students. https://students.unimelb.edu.au/academic-skills/graduate-research-services/writing-thesis-sections-part-2/shaping-the-research-question-and-hypothesis  

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How Do You Write a Hypothesis for a Research Paper: Tips and Examples

Crafting a well-defined hypothesis is a critical step in the research process, serving as the foundation for your study. A hypothesis not only guides your research design but also provides a clear focus for your investigation. In this article, we will explore the essential aspects of writing a strong hypothesis for a research paper, including its characteristics, formulation steps, types, and common pitfalls to avoid. Additionally, we will provide examples from various disciplines to illustrate what makes a hypothesis effective.

Key Takeaways

• A hypothesis is a testable statement that predicts the relationship between variables in your research.
• Clarity and precision are crucial for a strong hypothesis, ensuring that it is understandable and specific.
• A good hypothesis must be testable and falsifiable, meaning it can be supported or refuted through experimentation or observation.
• Formulating a hypothesis involves identifying a research problem, conducting a literature review, and clearly stating the expected outcome.
• Avoid common pitfalls such as overly complex hypotheses, vague language, and lack of testability to ensure your hypothesis is effective.

Understanding the Role of a Hypothesis in Research

Defining a hypothesis.

A hypothesis is a testable prediction about the relationship between two or more variables. It serves as a navigational tool in the research process, directing what you aim to predict and how. Crafting a thesis statement is crucial in the writing process, guiding research and shaping arguments.

Purpose and Importance of a Hypothesis

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

Hypothesis vs. Prediction

A hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. While hypotheses are sometimes called “educated guesses,” they should be based on previous observations, existing theories, scientific evidence, and logic. A hypothesis is not a prediction; rather, predictions are based on clearly formulated hypotheses.

Key Characteristics of a Strong Hypothesis

A robust hypothesis is essential for guiding your research effectively. Firstly, clarity and precision are paramount . Your hypothesis should be specific and unambiguous, providing a clear understanding of the expected relationship between variables. This ensures that your research question is well-defined and comprehensible.

Testability and falsifiability are also crucial. A hypothesis must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Additionally, it should be falsifiable, meaning that it can be proven wrong through evidence.

Lastly, relevance to the research question is vital. Your hypothesis should be grounded in existing research or theoretical frameworks, ensuring its applicability and significance to the field of study. This connection to prior research not only strengthens your hypothesis but also aligns it with the broader academic discourse.

Steps to Formulate a Hypothesis for a Research Paper

Identifying the research problem.

The first step in formulating a hypothesis is to clearly identify the research problem. This involves understanding the phenomenon or the relationships between variables that you wish to explore. A well-defined research problem sets the stage for a focused and effective hypothesis.

Conducting a Literature Review

Before you can formulate a hypothesis, it's essential to conduct a thorough literature review. This helps you understand what has already been studied and where gaps in the research exist. By reviewing existing literature, you can ensure that your hypothesis is both original and relevant.

Formulating the Hypothesis

Once you have identified the research problem and reviewed the literature, you can begin to formulate your hypothesis . A strong hypothesis should be clear, testable, and directly related to the research question. It often helps to frame your hypothesis as an 'if-then' statement, which clearly outlines the expected relationship between variables.

Types of Hypotheses in Research

Understanding the various types of hypotheses is crucial for crafting effective research. Each type serves a unique purpose and can significantly influence the direction and outcomes of your study. All hypotheses contrast with the null hypothesis , which posits that no significant relationship exists between the variables under investigation.

Common Pitfalls to Avoid When Writing a Hypothesis

When crafting a hypothesis for your research paper, it's crucial to steer clear of common mistakes that can undermine your work. Avoiding these pitfalls will help you create a robust and testable hypothesis that can withstand academic scrutiny.

Examples of Well-Written Hypotheses

In this section, we will explore various examples of well-crafted hypotheses to help you understand what makes a hypothesis strong and effective. By examining these examples, you can gain insights into the essential components that contribute to a robust hypothesis.

Testing and Refining Your Hypothesis

Once you have formulated your hypothesis, the next crucial step is to test and refine it. This process ensures that your hypothesis is robust and reliable, ultimately contributing to the validity of your research findings.

Testing and refining your hypothesis is a crucial step in your thesis journey. It ensures that your research is on the right track and that your findings are valid. To make this process easier, our Thesis Action Plan offers a structured approach to help you navigate through each stage with confidence. Don't let uncertainty hold you back. Visit our website to learn more and claim your special offer now !

Crafting a well-defined hypothesis is a critical step in the research process, serving as the foundation upon which your entire study is built. A clear and concise hypothesis not only guides your research design and methodology but also provides a focal point for data collection and analysis. By following the tips and examples provided in this article, researchers can develop robust hypotheses that are both testable and meaningful. Remember, a strong hypothesis is characterized by its specificity, clarity, and relevance to the research question. As you embark on your research journey, take the time to refine your hypothesis, as it will significantly impact the quality and credibility of your study. With careful consideration and thoughtful formulation, your hypothesis can pave the way for insightful and impactful research findings.

Frequently Asked Questions

What is a hypothesis in a research paper.

A hypothesis in a research paper is a statement that predicts the relationship between variables. It serves as a tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.

How do I formulate a strong hypothesis?

To formulate a strong hypothesis, ensure it is clear, precise, testable, and relevant to your research question. Conducting a thorough literature review can help you identify gaps in existing knowledge and formulate a hypothesis that addresses those gaps.

What is the difference between a hypothesis and a prediction?

A hypothesis is a testable statement about the relationship between two or more variables, while a prediction is a specific outcome that you expect to observe if the hypothesis is true. Predictions are often derived from hypotheses.

What are the types of hypotheses in research?

The main types of hypotheses in research are the null hypothesis, alternative hypothesis, directional hypothesis, and non-directional hypothesis. Each type serves a different purpose in statistical testing and research design.

Why is testability important in a hypothesis?

Testability is crucial in a hypothesis because it allows researchers to use empirical methods to determine whether the hypothesis is supported or refuted by the data. A hypothesis must be testable to be scientifically valid.

Can a hypothesis be revised?

Yes, a hypothesis can be revised based on new data, insights, or changes in the research focus. Revising a hypothesis is a common part of the scientific process as researchers refine their questions and methods.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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

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

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

What is a Research Hypothesis?

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

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

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

Importance of Hypothesis in Research

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

• A research hypothesis helps test theories.

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

• It serves as a great platform for investigation activities.

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

• Hypothesis guides the research work or study.

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

• Hypothesis sometimes suggests theories.

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

• It helps in knowing the data needs.

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

• The hypothesis explains social phenomena.

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

• Hypothesis provides a relationship between phenomena for empirical Testing.

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

• It helps in knowing the most suitable analysis technique.

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

Characteristics of a Good Research Hypothesis

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

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

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

Types of Research Hypotheses

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

01. Null Hypothesis

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

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

02. Alternative Hypothesis

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

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

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

03. Directional Hypothesis

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

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

04. Non-directional Hypothesis

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

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

05. Simple Hypothesis

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

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

06. Complex Hypothesis

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

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

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

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

07. Associative Hypothesis

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

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

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

08. Causal Hypothesis

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

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

09. Empirical Hypothesis

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

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

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

10. Statistical Hypothesis

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

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

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

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

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

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

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

Step 2: Conduct a literature review

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

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

Step 3: Formulate your research question

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

Step 4: Identify variables

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

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

Step 5: State the Null hypothesis

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

Step 6: Select appropriate methods for testing the hypothesis

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

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

Testing and Evaluating Hypotheses

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

• State your research hypothesis.

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

• Collect data strategically.

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

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

• Perform an appropriate statistical test.

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

• Decide if your idea was right or wrong.

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

• Share what you found.

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

The Role of QuestionPro to Develop a Good Research Hypothesis

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

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

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

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

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

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Research Guide: Scholarly Journals

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Hypothesis or Thesis

Looking for the author's thesis or hypothesis.

The image below shows the part of the scholarly article that shows where the authors are making their argument. 

(click on image to enlarge)

• The first few paragraphs of a journal article serve to introduce the topic, to provide the author's hypothesis or thesis, and to indicate why the research was done.  
• A thesis or hypothesis is not always clearly labeled; you may need to read through the introductory paragraphs to determine what the authors are proposing.
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• Last Updated: Aug 19, 2024 4:40 PM
• URL: https://libguides.greenriver.edu/scholarlyjournals

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Where to Put the Research Question in a Paper

Silke Haidekker has a PhD in Pharmacology from the University of Hannover. She is a Clinical Research Associate in multiple pharmaceutical companies in Germany and the USA. She now works as a full-time medical translator and writer in a small town in Georgia.

Of Rats and Panic Attacks: A Doctoral Student’s Tale

You would probably agree that the time spent writing your PhD dissertation or thesis is not only a time of taking pride or even joy in what you do, but also a time riddled with panic attacks of different varieties and lengths. When I worked on my PhD thesis in pharmacology in Germany many years back, I had  my  first panic attack as I first learned how to kill rats for my experiments with a very ugly tool called a guillotine! After that part of the procedure, I was to remove and mash their livers, spike them with Ciclosporin A (an immunosuppressive agent), and then present the metabolites by high-pressure liquid chromatography.

Many rats later, I had another serious panic attack. It occurred at the moment my doctoral adviser told me to write my first research paper on the Ciclosporin A metabolites I had detected in hundreds of slimy mashes of rat liver. Sadly, this second panic attack led to a third one that was caused by living in the pre-internet era, when it was not as easy to access information about  how to write research papers .

How I got over writing my first research paper is now ancient history. But it was only years later, living in the USA and finally being immersed in the language of most scientific research papers, that my interest in the art of writing “good” research papers was sparked during conferences held by the  American Medical Writers Association , as well as by getting involved in different writing programs and academic self-study courses.

How to State the Research Question in the Introduction Section

Good writing begins with clearly stating your research question (or hypothesis) in the Introduction section —the focal point on which your entire paper builds and unfolds in the subsequent Methods, Results, and Discussion sections . This research question or hypothesis that goes into the first section of your research manuscript, the Introduction, explains at least three major elements:

a) What is  known  or believed about the research topic?

B) what is still  unknown  (or problematic), c) what is the  question or hypothesis  of your investigation.

Some medical writers refer to this organizational structure of the Introduction as a “funnel shape” because it starts broadly, with the bigger picture, and then follows one scientifically logical step after the other until finally narrowing down the story to the focal point of your research at the end of the funnel.

Let’s now look in greater detail at a research question example and how you can logically embed it into the Introduction to make it a powerful focal point and ignite the reader’s interest about the importance of your research:

a) The Known

You should start by giving your reader a brief overview of knowledge or previous studies already performed in the context of your research topic.

The topic of one of my research papers was “investigating the value of diabetes as an independent predictor of death in people with end-stage renal disease (ESRD).” So in the Introduction, I first presented the basic knowledge that diabetes is the leading cause of end-stage renal disease (ESRD) and thus made the reader better understand our interest in this specific study population. I then presented previous studies already showing that diabetes indeed seems to represent an independent risk factor for death in the general population. However, very few studies had been performed in the ESRD population and those only yielded controversial results.

Example :  “It seems well established that there is a link between diabetic nephropathy and hypertensive nephropathy and end-stage renal disease (ESRD) in Western countries. In 2014, 73% of patients in US hospitals had comorbid ESRD and type 2 diabetes (1, 2, 3)…”

b) The Unknown

In our example, this “controversy” flags the “unknown” or “problematic” and therefore provides strong reasons for why further research is justified. The unknown should be clearly stated or implied by using phrases such as “were controversial” (as in our example), “…has not been determined,” or “…is unclear.” By clearly stating what is “unknown,” you indicate that your research is new. This creates a smooth transition into your research question.

Example :  “However, previous studies have failed to isolate diabetes as an independent factor, and thus much remains unknown about specific risk factors associated with both diabetes and ESRD .”

c) The Research Question (Hypothesis)

Your research question is the question that inevitably evolves from the deficits or problems revealed in the “Unknown” and clearly states the goal of your research. It is important to describe your research question in just one or two short sentences, but very precisely and including all variables studied, if applicable. A transition should be used to mark the transition from the unknown to the research question using one word such as “therefore” or “accordingly,” or short phrases like “for this reason” or “considering this lack of crucial information.”

In our example, we stated the research question as follows:

Example :  “Therefore, the primary goal of our study was to perform a Kaplan-Meier survival study and to investigate, by means of the Cox proportional hazard model, the value of diabetes as an independent predictor of death in diabetic patients with ESRD.”

Note that the research question may include the  experimental approach  of the study used to answer the research question.

Another powerful way to introduce the research question is to  state the research question as a hypothesis  so that the reader can more easily anticipate the answer. In our case, the question could be put as follows:

Example :  “To test the hypothesis that diabetes is an independent predictor of death in people with ESRD, we performed a Kaplan-Survival study and investigated the value of diabetes by means of the Cox proportional hazard model.”

Note that this sentence leads with an introductory clause that indicates the hypothesis itself, transitioning well into a synopsis of the approach in the second half of the sentence.

The generic framework of the Introduction can be modified to include, for example,  two  research questions instead of just one. In such a case, both questions must follow inevitably from the previous statements, meaning that the background information leading to the second question cannot be omitted. Otherwise, the Introduction will get confusing, with the reader not knowing where that question comes from.

Begin with your research purpose in mind

To conclude, here is my simple but most important advice for you as a researcher preparing to write a scientific paper (or just the Introduction of a research paper) for the first time: Think your research question through precisely before trying to write it down; have in mind the reasons for exactly why you wanted to do this specific research, what exactly you wanted to find out, and how (by which methods) you did your investigation. If you have the answers to these questions in mind (or even better, create a comprehensive outline ) before starting the paper, the actual writing process will be a piece of cake and you will finish it “like a rat up a drainpipe”! And hopefully with no panic attacks.

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Before submitting your master’s thesis or PhD dissertation to academic journals for publication, be sure to receive proofreading services (including research paper editing , manuscript editing , thesis editing , and dissertation editing ) to ensure that your research writing is error-free. Impress your journal editor and get into the academic journal of your choice.    

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Article Contents

Primacy of the research question, structure of the paper, writing a research article: advice to beginners.

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Thomas V. Perneger, Patricia M. Hudelson, Writing a research article: advice to beginners, International Journal for Quality in Health Care , Volume 16, Issue 3, June 2004, Pages 191–192, https://doi.org/10.1093/intqhc/mzh053

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Writing research papers does not come naturally to most of us. The typical research paper is a highly codified rhetorical form [ 1 , 2 ]. Knowledge of the rules—some explicit, others implied—goes a long way toward writing a paper that will get accepted in a peer-reviewed journal.

A good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper. Whatever relates to the research question belongs in the paper; the rest doesn’t. This is perhaps obvious when the paper reports on a well planned research project. However, in applied domains such as quality improvement, some papers are written based on projects that were undertaken for operational reasons, and not with the primary aim of producing new knowledge. In such cases, authors should define the main research question a posteriori and design the paper around it.

Generally, only one main research question should be addressed in a paper (secondary but related questions are allowed). If a project allows you to explore several distinct research questions, write several papers. For instance, if you measured the impact of obtaining written consent on patient satisfaction at a specialized clinic using a newly developed questionnaire, you may want to write one paper on the questionnaire development and validation, and another on the impact of the intervention. The idea is not to split results into ‘least publishable units’, a practice that is rightly decried, but rather into ‘optimally publishable units’.

What is a good research question? The key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community. The research question should be precise and not merely identify a general area of inquiry. It can often (but not always) be expressed in terms of a possible association between X and Y in a population Z, for example ‘we examined whether providing patients about to be discharged from the hospital with written information about their medications would improve their compliance with the treatment 1 month later’. A study does not necessarily have to break completely new ground, but it should extend previous knowledge in a useful way, or alternatively refute existing knowledge. Finally, the question should be of interest to others who work in the same scientific area. The latter requirement is more challenging for those who work in applied science than for basic scientists. While it may safely be assumed that the human genome is the same worldwide, whether the results of a local quality improvement project have wider relevance requires careful consideration and argument.

Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion.

In turn, each basic section addresses several topics, and may be divided into subsections (Table 1 ). In the Introduction, the authors should explain the rationale and background to the study. What is the research question, and why is it important to ask it? While it is neither necessary nor desirable to provide a full-blown review of the literature as a prelude to the study, it is helpful to situate the study within some larger field of enquiry. The research question should always be spelled out, and not merely left for the reader to guess.

Typical structure of a research paper

The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. Thus, this section should be specific, concrete, technical, and fairly detailed. The study setting, the sampling strategy used, instruments, data collection methods, and analysis strategies should be described. In the case of qualitative research studies, it is also useful to tell the reader which research tradition the study utilizes and to link the choice of methodological strategies with the research goals [ 3 ].

The Results section is typically fairly straightforward and factual. All results that relate to the research question should be given in detail, including simple counts and percentages. Resist the temptation to demonstrate analytic ability and the richness of the dataset by providing numerous tables of non-essential results.

The Discussion section allows the most freedom. This is why the Discussion is the most difficult to write, and is often the weakest part of a paper. Structured Discussion sections have been proposed by some journal editors [ 4 ]. While strict adherence to such rules may not be necessary, following a plan such as that proposed in Table 1 may help the novice writer stay on track.

References should be used wisely. Key assertions should be referenced, as well as the methods and instruments used. However, unless the paper is a comprehensive review of a topic, there is no need to be exhaustive. Also, references to unpublished work, to documents in the grey literature (technical reports), or to any source that the reader will have difficulty finding or understanding should be avoided.

Having the structure of the paper in place is a good start. However, there are many details that have to be attended to while writing. An obvious recommendation is to read, and follow, the instructions to authors published by the journal (typically found on the journal’s website). Another concerns non-native writers of English: do have a native speaker edit the manuscript. A paper usually goes through several drafts before it is submitted. When revising a paper, it is useful to keep an eye out for the most common mistakes (Table 2 ). If you avoid all those, your paper should be in good shape.

Common mistakes seen in manuscripts submitted to this journal

Huth EJ . How to Write and Publish Papers in the Medical Sciences , 2nd edition. Baltimore, MD: Williams & Wilkins, 1990 .

Browner WS . Publishing and Presenting Clinical Research . Baltimore, MD: Lippincott, Williams & Wilkins, 1999 .

Devers KJ , Frankel RM. Getting qualitative research published. Educ Health 2001 ; 14 : 109 –117.

Docherty M , Smith R. The case for structuring the discussion of scientific papers. Br Med J 1999 ; 318 : 1224 –1225.

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The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

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• Phyllis G. Supino EdD 3  

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A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

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The Nature and Logic of Science: Testing Hypotheses

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Supino, P.G. (2012). The Research Hypothesis: Role and Construction. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_3

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how to find the hypothesis in a research article

What is hypothesis in research article? – 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.

What is research hypothesis example? – For example, a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states, “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.”

What are examples of hypothesis? – › examples › science › hypot…

How do you find the hypothesis? – The first few paragraphs of a journal article serve to introduce the topic, to provide the author’s hypothesis or thesis, and to indicate why the research was done. A thesis or hypothesis is not always clearly labled; you may need to read through the introductory paragraphs to determine what the authors are proposing.

What chapter is hypothesis in research? – Chapter 4-RESEARCH HYPOTHESIS AND DEFINING VARIABLES.

What is hypothesis in research conclusion? – Martyn Shuttleworth, Lyndsay T Wilson646.6K reads. A research hypothesis (H1) is the statement created by researchers when they speculate upon the outcome of a research or experiment.

Is a hypothesis a question? – Definitions. A hypothesis is defined as an educated guess, while a research question is simply the researcher wondering about the world. Hypothesis are part of the scientific research method. They are employed in research in science, sociology, mathematics and more.

Is a hypothesis an IF THEN statement? – The hypothesis is an educated guess as to what will happen during your experiment. The hypothesis is often written using the words “IF” and “THEN.” For example, “If I do not study, then I will fail the test.” The “if’ and “then” statements reflect your independent and dependent variables.

What are the 3 types of hypothesis? – Types of hypothesis are: Simple hypothesis. Complex hypothesis. Directional hypothesis.

What is research hypothesis and its types? – A hypothesis is an approximate explanation that relates to the set of facts that can be tested by certain further investigations. There are basically two types, namely, null hypothesis and alternative hypothesis. A research generally starts with a problem.

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

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• Published: 10 September 2024

Concept and location neurons in the human brain provide the ‘what’ and ‘where’ in memory formation

• Sina Mackay   ORCID: orcid.org/0000-0001-6736-7550 1 ,
• Thomas P. Reber   ORCID: orcid.org/0000-0002-3969-9782 1 , 2 ,
• Marcel Bausch   ORCID: orcid.org/0000-0002-0188-3816 1 ,
• Jan Boström 3 ,
• Christian E. Elger   ORCID: orcid.org/0000-0002-2531-6701 1 &
• Florian Mormann   ORCID: orcid.org/0000-0003-1305-8028 1  

Nature Communications volume  15 , Article number:  7926 ( 2024 ) Cite this article

Metrics details

• Long-term memory
• Spatial memory

Our brains create new memories by capturing the ‘who/what’, ‘where’ and ‘when’ of everyday experiences. On a neuronal level, mechanisms facilitating a successful transfer into episodic memory are still unclear. We investigated this by measuring single neuron activity in the human medial temporal lobe during encoding of item-location associations. While previous research has found predictive effects in population activity in human MTL structures, we could attribute such effects to two specialized sub-groups of neurons: concept cells in the hippocampus, amygdala and entorhinal cortex (EC), and a second group of parahippocampal location-selective neurons. In both item- and location-selective populations, firing rates were significantly higher during successfully encoded trials. These findings are in line with theories of hippocampal indexing, since selective index neurons may act as pointers to neocortical representations. Overall, activation of distinct populations of neurons could directly support the connection of the ‘what’ and ‘where’ of episodic memory.

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Introduction.

The human medial temporal lobe (MTL) plays an essential role in memory. While many aspects of successful encoding and retrieval of mnemonic information have been extensively studied, the neuronal mechanisms that transform our perceptions into memories are as of yet mostly unknown. The main streams of information our brains need to access and combine in order to form new episodic memories are related to the question of “what” happened “where” and “when” 1 . A plethora of studies in rodents, non-human primates and humans have provided evidence for all three of these representations in the MTL 2 , 3 , 4 , 5 , 6 .

The rodent literature has revealed different types of spatial representations such as hippocampal place cells 7 , 8 and entorhinal grid cells 2 , 9 . There is also evidence of neurons in the rodent MTL that are modulated by the temporal sequence of task events 10 , 11 or interactions of space and elapsed time 12 . Buzsáki and Tingley proposed a model of hippocampal function that assumes a less domain-specific organization of information, by relying mainly on sequences of relevant events 13 . The strong parallels between place and time cells 10 are in line with this notion. Some sequential 14 and temporal representations including ramping cells 6 have also been shown to be reflected in neuronal firing patterns in the human MTL.

Spatial tuning in the form of grid cells 9 mapping two-dimensional space on a screen has been described in the entorhinal cortex (EC) of non-human primates 15 and has been linked to attention 16 . In humans, functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) studies during virtual navigation have likewise provided evidence for hexagonal grid representations within the EC 17 , 18 , 19 . Another study described entorhinal cells tuned to upcoming target locations along a virtual track in humans 20 . Nevertheless, human entorhinal neurons have been shown to generally not be involved in the processing of scenes and spatial information 21 .

Parahippocampal activity, on the other hand, has been linked to spatial navigation in 3D tasks on a laptop 22 . There is furthermore evidence of an allocentric coordinate system in the hippocampus of the moving macaque 23 and of both allocentric and egocentric representations in the human parahippocampal cortex (PHC) and hippocampus 24 . According to Bicanski and Burgess’ elaborate model of memory and navigation, allocentric maps are computed in the hippocampus with the help of bottom-up input from highly processed parietal sensory inputs. The system is alternating between a bottom-up and a top-down state so that mental maps can guide perception and can also constantly be updated with reference to moving objects or extended exploration 25 . Mediotemporal location- and view-specific neurons have been described in the human MTL ( 26 , but see ref. 27 ), and grid-cell-like neuronal activity in spatial navigation has likewise been reported in the human EC and hippocampus 28 , 29 .

A striking finding regarding selective hippocampal representations were visually selective neurons that represent semantic concepts 30 in different MTL regions including the hippocampus, amygdala, and EC. These neurons respond to the semantic content of a presented object or stimulus, e.g., to animals 31 , pieces of clothing 32 , or different pictures of a familiar person as well as to their written and spoken name 33 . They reflect subjective, conscious perception 34 , 35 , 36 , and can be activated in the absence of stimuli during imagery 37 , free recall 38 , or mental comparisons referencing their preferred concept 39 . These neurons were named concept cells and have been hypothesized to represent the semantic building blocks of episodic memory 40 . The human PHC differs from the other three MTL regions by showing earlier and less selective responses 41 and no invariance to written and spoken words 33 , by responding to scenes and spatial features of a stimulus 21 , and by being involved in spatial tasks 42 , 43 , 44 , 45 .

Previous studies have already addressed certain aspects of selective MTL activity in the context of memory tasks. These have yielded somewhat inconsistent results, such as significant modulation of firing rates during retrieval 46 , 47 , 48 , but no effects on firing rates during encoding 48 , 49 , 50 with the exception of a population of egocentric spatial cells 24 .

As pointed out by Wixted et al., one reason why it can be difficult to detect memory effects is that within a sparse coding system, those effects may only be exhibited by a small number of neurons 51 .

In this study, we wanted to assess subsequent memory effects within the sub-population of visually selective neurons. We analyzed neuronal activity during the encoding trials of an associative memory task with moderate difficulty, allowing us to compare subsequently remembered to forgotten trials. Since there was a spatial component to our memory task we were also able to search for spatially tuned neurons and their response modulation with respect to memory formation. Given that we were able to pre-select items, but not locations, based on a preceding screening session (see Methods), we expected a larger number of item responses than location responses.

Effects of experimental design parameters

Our associative memory paradigm involved sets of images presented at different locations within a 3 × 3 grid. The item-location associations had to be recalled later upon presentation of the image beneath an empty grid. Subjects had to tap the location on the grid where the image had been presented during encoding. We recorded data from 3681 single and multi-units in 13 neurosurgical patients with bilaterally implanted depth electrodes in the amygdala, hippocampus, EC and PHC (Table  S1 ). Stimuli were identified in a preceding screening procedure as likely response-eliciting (Methods). The task consisted of separate short runs where random combinations of images and spatial positions in a 3 × 3 grid on the screen had to be learned and then retrieved after a short distraction task (Fig.  1 ). Two variables were continuously adjusted in real-time during the task to achieve a performance of ~50%: the presentation duration during encoding, and the set size, i.e., the number of images that had to be memorized at once. The former could change after every trial, the set size had a greater impact on difficulty and was only adjusted after 3 consecutive high or low-performance trials (Methods). The presentation duration was modified in steps of 500 ms and held between 1.5 s and 3.5 s. Within this time window, the subject was required to tap the item location on the screen to verify that they had seen it. This triggered a green confirmation frame around the image but did not affect the presentation duration. Therefore, in all valid trials the reaction time (stimulus onset until confirmation tap) did not exceed the trial duration. We used linear mixed-effects models (Methods) to investigate the relationship between subsequent memory performance and the experimental parameters set size, reaction time and trial duration (in seconds). Our results revealed a significant effect of subsequent memory on set size ( β  = –0.27, P  < 10 –16 , subsequently forgotten trials were in larger sets) and on reaction time ( β  = 0.02, P  = 0.01, subsequently forgotten trials had longer reaction times), but not on trial duration ( β  = 0.004, P  = 0.9).

Top row: Each experimental session had a fixed duration of 35 min and consisted of consecutive runs of varying content and number of encoding trials, dynamically adapted to the subject’s performance. Bottom row: Composition of a single run. Each run consisted of encoding trials, a distractor task and retrieval trials. In this example, all locations were remembered correctly. Images used in this figure are licensed. Copyright © 2001 Thomas Reber and Getty Images. All rights reserved.

Subsequent memory effects in selective neurons

Due to the preceding screening procedure, we found highly significant fractions of neurons responding selectively to one or more items in all recorded brain regions (all P  < 10 –43 , binomial test, one-sided with n  = total number of neurons per region, k  = number of responsive neurons per region, P  = 0.001, corresponding to the alpha level of our response criterion, refer to “item responses”, Fig.  2 , Fig. S 1A for examples). In addition to the binomial test, we calculated the empirical size (i.e., the probability of falsely rejecting the null hypothesis if it is true) in each measured brain region. To this end, we compared the fraction of responsive items to 10,000 realizations of label-shuffled data and found empirical sizes of alpha  < 10 –4 in all measured brain regions (Fig. S 6 ). In this case, the binomial test (nominal size) and label-shuffling test (empirical size) produced consistent results. Responses to items were detected using a binwise rank-sum test ( P  < 0.001, see Methods). Whenever a neuron responded to one or more items, we computed the response activity for this neuron by averaging all trials containing a preferred item.

A Selective responses by single neurons (top: amygdala, bottom: hippocampus), separated based on correct vs. incorrect subsequent retrieval. Solid lines (lower panels): response to the preferred item. Dashed lines: average response to all non-preferred items (cf. Fig. S 1A ). B Responses of single neurons in the PHC to spatial locations within the presentation grid. Solid lines (lower panels): response to the preferred item locations, which in the lower example includes the entire bottom row of the grid. Dashed lines: average response to all non-preferred locations (cf. Fig. S 1B ) Subsequent memory effects per neuron were statistically assessed using a one-sided Wilcoxon rank-sum test for the time window of 0 to 1500 ms. Statistically significant effects were found for the two item neurons (top, P  = 0.008, Z  = 2.40; bottom, P  = 0.02, Z  = 2.04), but not for the two location neurons (both P  > 0.1, Z  < 0.95). Source data are provided in a git repository (see Data Availablity).

Responses to grid locations were computed in the same way in that a neuron had to show a significant response to one or more of the nine locations in which an image was presented throughout the experiment. We furthermore found the responsive cells to be selective. The vast majority of these neurons responded to half or fewer of the presented items (Amygdala: 99%, Hippocampus: 96%, EC: 100%, PHC: 84%), or item locations (PHC: 81%, see below). There were two hippocampal target locations (anterior and posterior hippocampus), which were grouped together in all analyses. The fractions of responsive neurons in these two hippocampal regions did not differ within patients (item neurons: T(12)  =  –0.64, P  = 0.54, location neurons: T (12) = –1.53, P  = 0.15, paired t -test). Figure  3 shows that item responses were modulated by subsequent memory performance in the amygdala, hippocampus, and EC, in that the responses to subsequently correctly placed items were more pronounced. This effect occurred at a latency of 239 to 1249 ms in the amygdala, 531 to 796 ms in the hippocampus, and 491 to 618 ms in the EC, i.e., generally after the initial peak activity (250 to 500 ms, see also ref. 41 ). Notably, this effect was not observed for item responses in the PHC.

Group activity as averaged convolved firing rates during responses in encoding trials, aligned to stimulus onset. For neurons responding to several items or locations, all trials featuring a response-eliciting item or location were averaged. Shaded areas denote standard errors of bootstrapped means. Subsequently remembered (blue) and forgotten (red) trials were compared using a cluster permutation test (indicated as dark bars along x -axis, * P  < .05, ** P  < .01, *** P  < .001, see Methods). The n indicated in each panel is the number of neurons fulfulling the (non-) response criterion. A responsive neuron can be represented in the left and middle column, so that the n summed across one row may exceed the total population. Each row represents one of the four recorded brain regions. Left column: all neurons with a significant response to at least one item. Maximum effect sizes and cluster P values: Amygdala (Amy) d  = 0.36, P  < 10 –4 , Hippocampus (Hipp) d  = 0.33, P  = 0.010 and EC d  = 0.21, P  = 0.038. Center column: all neurons with a significant response to at least one spatial location. Effect size in PHC: d  = 0.60, P  < 10 –4 (aligned to stimulus onset), d  = 0.37, P  < 10 –4 (aligned to confirmation tap). Right column: all remaining neurons. Effect size in Amygdala: d  = −0.10, P  = 0.046 (pre stimulus onset), EC: d  = 0.23, P  = 0.031 (pre stimulus onset) and d  = 0.14, P  = 0.005 (post stimulus onset). The right column is also displayed in Fig.  S3 , with adjusted y -axis ranges. Source data are provided in a git repository (see Data Availablity).

We also investigated responses to spatial locations, i.e., to squares within the presentation grid. Using a binomial test, we found significant fractions of neurons responding to locations in the amygdala, hippocampus, and PHC (all P  < 10 –23 , one-sided with n  = total number of neurons per region, k  = number of responsive neurons per region, P  = 0.001, corresponding to the alpha level of our response criterion). However, it is important to note that the nominal size (significance level) might not always align with the empirical size of the test. Specifically, to test whether more than 0.001 of cells could be expected to be responsive by chance, we compared the measured fractions to 10,000 iterations of label-shuffled data. We only found a significant empirical size for the PHC (0.0027), but not amygdala (0.23) or hippocampus (0.81, Fig. S 6 ). We then statistically compared the proportion of location cells found in PHC to that in all other regions. Out of all parahippocampal neurons, 8.80% responded to at least one of the squares in the presentation grid (“location responses”, Fig.  2 , Fig. S 2 ), a significantly higher percentage than in the amygdala or hippocampus (chi-square test, both P  < 10 –8 , χ 2  = 40.50 and 53.08, Fig. S 2 ). These location cells also showed a subsequent memory effect. As with the item responses described above, firing rates were higher in subsequently remembered trials. This effect was found in a later time window (1059 to 1444 ms), subsequent to or partially overlapping with the effects in item responses in the amygdala, hippocampus and EC. Since this effect overlaps with the median response latency of 1.16 s (image onset until confirmation tap), we also evaluated the same responses aligned to the response tap (Fig.  3 , bottom panel). The reactivation did not seem to be driven by motor processes since it took place after the tap (25 to 506 ms) and it was significantly modulated by subsequent memory performance for both alignments.

In the amygdala and EC, we saw a subsequent memory effect in neurons exhibiting no significant item or location responses. The effect sizes of d  = 0.10 (Amygdala) and d  = 0.14 (EC), however, were considerably smaller than that of the memory effects previously described for item and location responses, which ranged from 0.21 to 0.59 (Fig.  3 ). Since the two traces are difficult to discern in column 3 of Fig.  3 , refer to Fig.  S3 for the same plots with an adjusted y -axis.

Neural activity during delay periods

Between the encoding and retrieval trials of each run, we prevented any rehearsal strategies by adding a 15-s counting task (see Methods). We were nevertheless interested in whether neurons were reactivated during this delay period. For each neuron responding to exactly one stimulus, we computed the average firing rate normalized to the 500 ms preceding stimulus onsets in encoding trials. Two averages were calculated for each neuron, one across counting episodes during which the preferred item’s location was remembered and one for episodes during which it was forgotten. We then compared those two values across neurons using a paired t -test and found no significant differences ( T (191) = –0.68, P  = 0.50). This analysis was repeated for each brain region (all P  > 0.1) and also for location-selective neurons in the PHC ( T (26) = –0.21, P  = 0.84).

Control analyses

Since a small fraction of neurons were classified as both item and location neurons, we repeated the main analyses shown in Fig.  3 after excluding these neurons and found the effects to be largely identical (Fig. S 4 ).

Given that images were shown repeatedly across trials, we tested for effects of adaptation or memory interference from previous trials by performing a split-half analysis (first half of trials vs. second half of trials). Both halves showed quantitatively similar results to those shown in Fig.  3 (data not shown here).

Furthermore, we verified whether the preferred stimuli of selected cells remained the same during the retrieval trials. Indeed, firing rates in response to preferred items were higher in all four recorded brain regions (all P  < 0.001 for amygdala, hippocampus, EC, and PHC, two-tailed signrank test, see Fig. S 5 ) during retrieval. The same was true for location cells in the PHC ( P  < 0.001), supporting the idea that location cells are in fact encoding location and not merely combinations or associations. For these tests, normalized mean firing rates were computed during the 1000 ms leading up to the response tap in retrieval trials, and compared using signed-rank tests (Fig. S 5 ).

Our mnemonic recall of an experienced event or episode comprises among other things the information of where the event or episode happened, who or what was involved, and when it took place. The MTL’s task during the encoding of such an episodic memory thus consists of associating corresponding representations at the neuronal level. In this study, we operationalized this association of “where” and “what/who”, i.e., of item and location information, in the form of an associative memory task. The adaptive design increased difficulty to the point where the participants were not able to remember all information. Furthermore, every delay period was filled with a 15 s backwards counting task. This resulted in a high memory load and prevented the rehearsal of the learned associations. Based on these features, the completion of this task requires long-term memory 52 , 53 .

Two independent studies have reported large fractions of visually selective neurons in the amygdala, hippocampus and EC to exhibit invariance with respect to different visual representations of the same semantic concept 33 , 54 (72% across amygdala, hippocampus, and EC, and 77% across amygdala and hippocampus, respectively), A previous study from our own group required a high level of abstraction for neurons to qualify as concept cells 39 , which, again, was the case for the majority (53%) of neurons across the same three regions. We therefore expect a majority of item neurons to qualify as concept cells.

Previous studies investigating memory encoding in the human MTL at the neuronal level have shown subsequent memory effects only at the population-code level. These studies find that the majority of memory-predictive cells show increased firing rates during encoding when information was processed that could later be recalled 55 or recognized 56 . However, these effects have largely been absent in selectively responsive single neurons ( 6 , 48 , 49 , 50 but see ref. 24 ). Our results are in line with the idea that concept cells represent the building blocks of memory 40 . Not only do we see subsequent memory effects; they are also restricted to sparse, selective neurons 51 . In the amygdala, hippocampus, and EC this applies exclusively to item neurons, suggesting that they provide the “who/what” information in associative memory encoding. Following this theory and considering the analogous effects with regard to “where” representations in parahippocampal location neurons, this population could provide spatial information for memory encoding. The PHC being home to location neurons is in agreement with a number of other studies tying parahippocampal activity to spatial tasks 42 , 43 , 44 , 45 . Another property of the PHC that is consistent with earlier findings is its lower degree of selectivity 21 , 41 , which we see in responses both to locations and to items (Fig. S 2 ).

The hippocampal memory indexing theory 57 , 58 offers an interesting framework with respect to our findings. This theory’s core idea is that in order to encode an event, a hippocampal code, or “index”, is created which points to neocortical networks where information associated with the event is stored 58 . Through coordinated activation of index neurons for different concepts, synaptic connections between different index neurons or between their respective referenced neocortical networks could be strengthened via spike-timing-dependent plasticity 59 . A thorough and extended activation of index neurons representing concepts could thus facilitate a connection to neurons representing a spatial location. In light of our data, we see the memory-predictive item neurons in the hippocampus, and also the amygdala and EC, as potential pointers to neocortical semantic content. They could fulfill the role of the “index” according to the hippocampal indexing theory and support memory encoding. Refs. 13 , 60 posit that these types of pointers should be content-free and part of pre-defined sequences that can be assigned as needed to contents such as experienced events. We routinely identify concept cells in screening sessions to investigate them in follow-up experiments later during the day and find their responses to the same stimuli to be trackable for hours or even days using standard monotrode microwire recordings. These concept neurons, therefore, appear to be permanently and invariantly (i.e., independently of context) assigned to a semantic content and not easily re-assignable to new perceived concepts on the fly (but see ref. 61 ). This observation of invariance over time ties in with the general idea that the human memory system might be optimized for creating semantic associations rather than ordered sequences.

It is worth noting that location neurons in the PHC showed a subsequent memory effect, but that there was no corresponding effect in the respective item neurons in this brain region (Fig.  3 ). Since the fractions of location cells in the amygdala and hippocampus were not statistically significant in the label-shuffled permutation test, any response activity to a specific grid location in these regions (Fig.  3 ) is likely an epiphenomenon of response activity to the visual stimuli. We only found a significantly large population of location cells in the PHC, which was also the only brain region to respond more strongly to the same preferred locations during retrieval as during encoding (Fig. S 5 ). Together with the aforementioned distinctive features of the PHC, this could indicate that the parahippocampal location neurons are not pointers, but actual neocortical representations based on a population code 21 .

The firing behavior of entorhinal neurons was of special interest since this region is closely linked to both PHC and hippocampus. In this experiment, we observed firing behavior in the EC to resemble that in the amygdala and hippocampus, rather than the PHC. Some previous findings point towards entorhinal involvement in spatial navigation 20 , 24 , 48 , yet we found hardly any responses to spatial locations. One possible explanation for this discrepancy is the lack of egocentric navigation required in our task. The layout on the screen is more reminiscent of a map, which is rather linked to semantic knowledge 62 . In another study, entorhinal neurons did not show the same strong preference for landscapes as the PHC 21 , which is in line with our results.

The finding that the subsequent memory effect in location responses occurred in a later time window than that for item responses could result from the way in which humans process “what” and “where” information. There are several linguistic models of thematic hierarchy which differ slightly depending on the phenomena they aim to explain. They rank semantic elements of sentences such as the agents, experiencers, goals, location, instruments, etc. according to their prominence. Almost all of them rank location in the lowest category 63 . Furthermore, there is evidence of a universal, natural order in which humans convey information when forced to use gestures instead of the spoken language they are used to. In a study where scenes with one stationary and one moving object were watched and then reproduced, the objects were acted out before the spatial movement 64 . Perhaps the order of the effects we see on a neuronal level, namely item before location, reflects the architecture of internally generated narratives, where information components are processed in descending order of prominence.

A third stream of information that has been suggested to be integrated in the process is temporal, i.e., the aspect of “when” something happened 1 . While some researchers have described neuronal activity related to passing time 6 , it is difficult to assess temporal activity entirely independently of other relevant aspects of the experimental task or the subject’s behavior 12 , 13 , 60 .

Episodic and semantic memory are the two constituents of declarative memory, which, unlike implicit memory, requires explicit conscious perception of sensory input. The activity of concept neurons in hippocampus, amygdala, and EC indeed has been shown to follow conscious perception rather than stimulus input 36 .

Note that due to its experimental task implementation, our study was not designed to investigate memory consolidation, a process during which memory traces are stabilized and presumably transferred to the neocortex to eventually become hippocampus-independent. Instead, we deliberately prevented active rehearsal between the encoding and retrieval phase by means of our mathematical distraction task. It can be hypothesized, however, that mediotemporal concept neurons and possibly also parahippocampal location cells involved in our everyday experiences are reactivated during periods of memory consolidation, e.g., during slow-wave sleep 65 . Such a reactivation of pointer neurons during an offline consolidation state with no sensory input could likewise facilitate the strengthening of synaptic connections between the neocortical representations referenced by mediotemporal pointer cells. Future studies will be needed to investigate this hypothesis.

Participants and setting

We recorded data from 13 in-patients (20–62 years old, 8 female, 5 male) with drug-resistant epilepsy who had undergone invasive surgery for seizure localization. Due to the implanted electrodes that were wired to the recording system, the patients were confined to their beds for around 7–10 days. During this time, we ran our experiments with them in their hospital beds. They sat up at least 45° and performed the task on a touch-screen laptop on a tray in front of them. All participants gave their written informed consent, and the study was approved by the Medical Institutional Review Board of the University of Bonn.

Screening procedure

Each recording was preceded by a screening session in the morning of the same day in order to identify response-eliciting images. This screening session was either an object screening (OS) with a fixed set of 100 images of commonly known objects and animals described in a previous publication 32 , or a customized person screening (PS) with an individual set of 100–150 images of the participant's friends and family, public figures, familiar places or objects related to their hobbies and jobs. These screenings were very similar in experimental design to the procedures described in previous publications from our own and other groups 30 , 31 , 33 , 35 , 41 , 66 . Each image was shown 10 (OS) or 6 (PS) times and a simple decision task was performed after every presentation (OS: “Is the object man-made?”, PS: “Does the image contain a face?”). The repeated presentation of each image allowed for the detection of statistically significant responses to certain images. The images shown during the screening covered a large number of semantic concepts, and the stimuli selected for our main task generally depicted different objects, places or people.

The spatial framework of the main experiment was a 3 × 3 grid on a touchscreen laptop, and each image was presented in one of the 9 squares. The task was to remember and retrieve the spatial locations of the images. Each session was limited to 35 min and was divided into runs (Fig.  1 , top row), where the total number of runs varied depending on the speed and performance of the patient. Within each run, a subset of images was shown, one at a time, at different, randomly assigned locations within the grid (Fig.  1 , bottom row). The participant was asked to confirm every image location by tapping it within the presentation time window (1.5–3.5 s). Whenever the correct square was tapped, a green square appeared along its outline for the remainder of the presentation duration. Trials with off-target or missing confirmation tap were considered invalid and were excluded from the analyses assessing memory effects. Those trials also triggered an immediate dissonant feedback sound and, in case of a misplaced tap, a red square around the tapped, empty square. Following the encoding trials, a random number between 80 and 100 appeared on the screen and the participant counted down vocally in steps of three until the number disappeared after 15 s. The last part of each run consisted of recall trials, where one by one the items from the beginning of the run were shown in shuffled order below an empty grid and the participant tried to recall and tap each item location. After each run, a feedback screen showed the percentage of correct answers. Retrieval trials and initiation of runs were self-paced. For each new run, a new subset of images was drawn from the item pool, evening out presentation counts, and filling remaining slots by random selection. The locations were assigned randomly. In order to obtain similar numbers of subsequently remembered and forgotten trials, we adjusted the difficulty in two ways. Each run was classified either as high-performance (>65% correct), low-performance (<35% correct) or medium performance (35–65% correct). Encoding presentation duration was initially 2 s and was increased following low-performance trials and decreased following high-performance trials. Values changed in steps of 0.5 s and were capped at 1.5 s and 3.5 s. Furthermore, after 3 consecutive low-performance trials of equal set size, the set size of the next run was decreased by 1 and accordingly increased by 1 after 3 consecutive high-performance trials. Whenever the set size changed, presentation duration was reset to 2 s. The minimum number of images per run was 1, the theoretical maximum was the total item pool size for the session (up to 8, details below), which was only reached in one session but was not a limiting factor. The initial set size was always 2, ensuring a low difficulty and therefore high motivation for most participants. This resulted in relatively high performance during the initial runs, and overall we recorded more correct than incorrect trials (13.3 vs. 10.7 on average).

The item pool size for an entire recording session was between 4 and 8 and was based on expected patient performance. Low memory performance would result in smaller set sizes and thus lower overall trial counts within the time limit of 35 min. Aiming for similar numbers of presentations per image across patients, we determined smaller image pools for putative low-performance participants. The mean number of trials per session was 168.61 ( sd 49.88, range 64–278), the number of runs was 58.83 ( sd 12.30, range 34–83), and the mean set size was 3.18 ( sd 1.17, range 1–7).

We did not expect epilepsy-related neuronal firing to substantially affect our results. As shown by ref. 67 , such interference should be minimal and should only affect small, specific sub-populations during recall.

Electrophysiological recordings

All data presented here were recorded from implanted Behnke-Fried depth electrodes (AdTech, Racine, WI), inserted through the hollow clinical macro electrodes, and protruding from the tips by ~4 mm. The microelectrodes were grouped in bundles of 8 recording wires plus one reference wire per macro electrode. The standard bilateral implantation scheme included 5 bundles per hemisphere, 1 in amygdala, 2 in hippocampus, 1 in EC, and 1 in PHC, adding up to 80 recording microwires in total. The continuous signal was recorded at 32 kHz on a Neuralynx ATLAS system (Bozeman, MT). Spikes were extracted and semiautomatically sorted using Combinato 68 . This software includes several mechanisms to automatically detect and reject artifacts: removal of spikes during extremely high firing rates, high amplitude events, overlapping spikes, and events detected concurrently on many channels. Automatically pre-sorted units were manually verified, adjusted where necessary, and classified as single units (SU), multi-units (MU), or artifacts based on spike shape and variance, signal-to-noise ratio (SNR), the inter-spike interval distribution of each cluster, and presence of a refractory period for the single units. We calculated the SNR for each single and multi-unit. It was defined as the mean spike amplitude divided by the median absolute signal. Single units (median SNR 2.85) had significantly greater SNRs than multi-units (median SNR 2.08, P  < 10 –38 , rank-sum test). We recorded a total of 3681 neurons (1816 single units and 1865 multi-units) in 44 sessions, specifically 1117 units from amygdala, 1391 from hippocampus, 571 from EC and 602 from PHC.

Responsiveness and statistical tests

To determine responsiveness, we used an established criterion based on a binwise rank-sum test (100 ms windows, 50% overlap, 0–1000 ms post stimulus presentation) with Simes correction for 19 bins 41 and a significance level of alpha  = 0.001. Whenever responses were compared with regard to subsequent memory, we calculated one average response per neuron across respective trials. In the case of several response-eliciting items, all trials depicting any of those items were averaged. The same applied to location responses.

The number of responsive neurons was then tested against chance levels for each brain region, using two different approaches. One was a parametric approach, a binomial test where the occurrence rate P was set to P  = 0.001, the same as the alpha level in the response criterion described above. The other was a permutation-based approach, where item labels or location labels were shuffled 10,000 times, resulting in a distribution of 10,000 proportions. The P -value was calculated as the fraction of label-shuffled data points that were more extreme than the measured data including half of shuffled data points that were equal to the measured value.

The population responses in Fig.  3 were then compared using a cluster permutation test 69 : first the responses during subsequently remembered and forgotten trials were compared at every time point, resulting in temporal clusters of significant differences (paired t -test P  < 0.05) between the two conditions. The same was done in 10,000 iterations of label-shuffled data. Finally, the cluster sizes from the true data were ranked against the distribution of cluster sizes from the shuffled data. Only clusters whose size ranked in the top 5% were considered and marked by the dark horizontal lines in Fig.  3 .

Linear mixed-effect models

We investigated whether there was a relationship between subsequent memory performance and the three experimental parameters set size, trial duration, and reaction time. To this end, we first calculated two means for each of these parameters per session, one across all subsequently remembered encoding trials and one across all subsequently forgotten encoding trials. Accordingly, subsequent memory performance was used as the predictor to model the relationship. To account for individual differences between patients and across sessions, we fitted linear mixed-effects models with random intercepts and slopes at the level of patient ID and session index (nested within patient ID). The reported estimates β in the Results section refer to the fixed slopes (i.e., average slopes across all patients).

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data used to produce all figures is available in the git repository detailed below, within the directory “source_data”.  Source data are provided with this paper.

Code availability

The code for producing all figures is available along with the source data, in the git repository https://github.com/s-mackay/grid_memory .

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Acknowledgements

We thank all patients for their participation, Johannes Niediek for discussion, and Gert Dehnen for technical assistance. This research was supported by the Volkswagen Foundation (86 506), the German Ministry of Education and Research (BMBF 031L0197B), the German Research Council (MO930/4-2, MO930/15-1, SPP 2205, SPP 2411, SFB 1089) and a NRW Network Grant (iBehave).

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Department of Epileptology, University Hospital Bonn, Bonn, Germany

Sina Mackay, Thomas P. Reber, Marcel Bausch, Christian E. Elger & Florian Mormann

Faculty of Psychology, UniDistance Suisse, Brig, Switzerland

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Contributions

Conceptualization: T.P.R., F.M., Data acquisition: S.M., T.P.R., M.B., Analyses: S.M., Methodology: S.M., F.M., Patient recruitment: C.E.E., F.M., Neurosurgical procedures: J.B., F.M., Funding acquisition: F.M., Project administration: F.M., Supervision: F.M., Writing – original draft: S.M., F.M., Writing – review & editing: S.M., F.M., T.P.R., M.B., C.E.E., J.B.

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Correspondence to Florian Mormann .

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Mackay, S., Reber, T.P., Bausch, M. et al. Concept and location neurons in the human brain provide the ‘what’ and ‘where’ in memory formation. Nat Commun 15 , 7926 (2024). https://doi.org/10.1038/s41467-024-52295-5

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DOI : https://doi.org/10.1038/s41467-024-52295-5

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