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What is a Hypothesis – Types, Examples and Writing Guide

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Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

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• Knowledge Base

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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• Scientific Methods

What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

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

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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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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2.4: Developing a Hypothesis

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Learning Objectives

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.2 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202.
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92.
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168.

2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.3  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

• Zajonc, R. B. (1965). Social facilitation.  Science, 149 , 269–274 ↵
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis Copyright © 2022 by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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What is and How to Write a Good Hypothesis in Research?

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One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

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.

One hypothesis example would be 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."

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. It is only at this point that researchers 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 a number of 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 wisdom 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.

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.

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.

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

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

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 that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• 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 sample of the population 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."
• Complex hypothesis: "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."

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:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

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 it would be impossible or difficult to  conduct an experiment . 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 then be used to look at how the variables are related. This type of 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.

A Word From Verywell

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.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

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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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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What Are the Elements of a Good Hypothesis?

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A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable . While you could consider any prediction of an outcome to be a type of hypothesis, a good hypothesis is one you can test using the scientific method. In other words, you want to propose a hypothesis to use as the basis for an experiment.

Cause and Effect or 'If, Then' Relationships

A good experimental hypothesis can be written as an if, then statement to establish cause and effect on the variables. If you make a change to the independent variable, then the dependent variable will respond. Here's an example of a hypothesis:

If you increase the duration of light, (then) corn plants will grow more each day.

The hypothesis establishes two variables, length of light exposure, and the rate of plant growth. An experiment could be designed to test whether the rate of growth depends on the duration of light. The duration of light is the independent variable, which you can control in an experiment . The rate of plant growth is the dependent variable, which you can measure and record as data in an experiment.

Key Points of Hypothesis

When you have an idea for a hypothesis, it may help to write it out in several different ways. Review your choices and select a hypothesis that accurately describes what you are testing.

• Does the hypothesis relate an independent and dependent variable? Can you identify the variables?
• Can you test the hypothesis? In other words, could you design an experiment that would allow you to establish or disprove a relationship between the variables?
• Would your experiment be safe and ethical?
• Is there a simpler or more precise way to state the hypothesis? If so, rewrite it.

What If the Hypothesis Is Incorrect?

It's not wrong or bad if the hypothesis is not supported or is incorrect. Actually, this outcome may tell you more about a relationship between the variables than if the hypothesis is supported. You may intentionally write your hypothesis as a null hypothesis or no-difference hypothesis to establish a relationship between the variables.

For example, the hypothesis:

The rate of corn plant growth does not depend on the duration of light.

This can be tested by exposing corn plants to different length "days" and measuring the rate of plant growth. A statistical test can be applied to measure how well the data support the hypothesis. If the hypothesis is not supported, then you have evidence of a relationship between the variables. It's easier to establish cause and effect by testing whether "no effect" is found. Alternatively, if the null hypothesis is supported, then you have shown the variables are not related. Either way, your experiment is a success.

Need more examples of how to write a hypothesis ? Here you go:

• If you turn out all the lights, you will fall asleep faster. (Think: How would you test it?)
• If you drop different objects, they will fall at the same rate.
• If you eat only fast food, then you will gain weight.
• If you use cruise control, then your car will get better gas mileage.
• If you apply a top coat, then your manicure will last longer.
• If you turn the lights on and off rapidly, then the bulb will burn out faster.
• Null Hypothesis Definition and Examples
• Six Steps of the Scientific Method
• What Is a Hypothesis? (Science)
• Understanding Simple vs Controlled Experiments
• Dependent Variable Definition and Examples
• How To Design a Science Fair Experiment
• Null Hypothesis Examples
• Scientific Method Vocabulary Terms
• Scientific Method Flow Chart
• What Are Independent and Dependent Variables?
• Definition of a Hypothesis
• Scientific Variable
• What Is an Experiment? Definition and Design
• What Is a Testable Hypothesis?
• What Is a Control Group?

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Characteristics Of A Good Hypothesis​

What exactly is a hypothesis.

A hypothesis is a conclusion reached after considering the evidence. This is the first step in any investigation, where the research questions are translated into a prediction. Variables, population, and the relationship between the variables are all included. A research hypothesis is a hypothesis that is tested to see if two or more variables have a relationship. Now let’s have a look at the characteristics of a  good hypothesis.

 Characteristics of

A good hypothesis has the following characteristics.

 Ability To Predict

Closest to things that can be seen, testability, relevant to the issue, techniques that are applicable, new discoveries have been made as a result of this ., harmony & consistency.

• The similarity between the two phenomena.
• Observations from previous studies, current experiences, and feedback from rivals.
• Theories based on science.
• People’s thinking processes are influenced by general patterns.
• A straightforward hypothesis
• Complex Hypothesis
• Hypothesis  with a certain direction
•  Non-direction Hypothesis
• Null Hypothesis
• Hypothesis of association and chance

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What is Hypothesis? Definition, Meaning, Characteristics, Sources

• Post last modified: 10 January 2022
• Reading time: 18 mins read
• Post category: Research Methodology

• What is Hypothesis?

Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.

As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.

In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.

Table of Content

• 1 What is Hypothesis?
• 2 Hypothesis Definition
• 3 Meaning of Hypothesis
• 4.1 Conceptual Clarity
• 4.2 Need of empirical referents
• 4.3 Hypothesis should be specific
• 4.4 Hypothesis should be within the ambit of the available research techniques
• 4.5 Hypothesis should be consistent with the theory
• 4.6 Hypothesis should be concerned with observable facts and empirical events
• 4.7 Hypothesis should be simple
• 5.1 Observation
• 5.2 Analogies
• 5.4 State of Knowledge
• 5.5 Culture
• 5.6 Continuity of Research
• 6.1 Null Hypothesis
• 6.2 Alternative Hypothesis

Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.

The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).

Hypothesis Definition

A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart

Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black

Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt

A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.

• At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data.
• Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event.
• Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true. It can get disproven. In other words, hypothesis need not always be a true proposition.
• Hypothesis, in a sense, is an attempt to present the interrelations that exist in the available data or information.
• Hypothesis is not an individual opinion or community thought. Instead, it is a philosophical means which is to be used for research purpose. Hypothesis is not to be considered as the ultimate objective; rather it is to be taken as the means of explaining scientifically the prevailing situation.

The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.

Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.

Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.

• Population growth moderates with the rise in per capita income.
• Sales growth is positively linked with the availability of credit.
• Commerce education increases the employability of the graduate students.
• High rates of direct taxes prompt people to evade taxes.
• Good working conditions improve the productivity of employees.
• Advertising is the most effecting way of promoting sales than any other scheme.
• Higher Debt-Equity Ratio increases the probability of insolvency.
• Economic reforms in India have made the public sector banks more efficient and competent.
• Foreign direct investment in India has moved in those sectors which offer higher rate of profit.
• There is no significant association between credit rating and investment of fund.

Characteristics of Hypothesis

Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:

Conceptual Clarity

Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.

The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.

A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.

A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.

For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.

For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.

For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.

While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.

It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.

A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.

If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.

This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”

If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.

Sources of Hypothesis

Hypotheses can be derived from various sources. Some of the sources is given below:

Observation

State of knowledge, continuity of research.

Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.

Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.

This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.

An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.

Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.

The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.

Null and Alternative Hypothesis

Null hypothesis.

The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.

Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.

Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .

Alternative Hypothesis

Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .

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Scientific Hypotheses: Writing, Promoting, and Predicting Implications

Armen yuri gasparyan.

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

Lilit Ayvazyan

2 Department of Medical Chemistry, Yerevan State Medical University, Yerevan, Armenia.

Ulzhan Mukanova

3 Department of Surgical Disciplines, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Marlen Yessirkepov

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

George D. Kitas

5 Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.

Scientific hypotheses are essential for progress in rapidly developing academic disciplines. Proposing new ideas and hypotheses require thorough analyses of evidence-based data and predictions of the implications. One of the main concerns relates to the ethical implications of the generated hypotheses. The authors may need to outline potential benefits and limitations of their suggestions and target widely visible publication outlets to ignite discussion by experts and start testing the hypotheses. Not many publication outlets are currently welcoming hypotheses and unconventional ideas that may open gates to criticism and conservative remarks. A few scholarly journals guide the authors on how to structure hypotheses. Reflecting on general and specific issues around the subject matter is often recommended for drafting a well-structured hypothesis article. An analysis of influential hypotheses, presented in this article, particularly Strachan's hygiene hypothesis with global implications in the field of immunology and allergy, points to the need for properly interpreting and testing new suggestions. Envisaging the ethical implications of the hypotheses should be considered both by authors and journal editors during the writing and publishing process.

INTRODUCTION

We live in times of digitization that radically changes scientific research, reporting, and publishing strategies. Researchers all over the world are overwhelmed with processing large volumes of information and searching through numerous online platforms, all of which make the whole process of scholarly analysis and synthesis complex and sophisticated.

Current research activities are diversifying to combine scientific observations with analysis of facts recorded by scholars from various professional backgrounds. 1 Citation analyses and networking on social media are also becoming essential for shaping research and publishing strategies globally. 2 Learning specifics of increasingly interdisciplinary research studies and acquiring information facilitation skills aid researchers in formulating innovative ideas and predicting developments in interrelated scientific fields.

Arguably, researchers are currently offered more opportunities than in the past for generating new ideas by performing their routine laboratory activities, observing individual cases and unusual developments, and critically analyzing published scientific facts. What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way. 3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant online databases and promotion platforms.

Although hypotheses are crucially important for the scientific progress, only few highly skilled researchers formulate and eventually publish their innovative ideas per se . Understandably, in an increasingly competitive research environment, most authors would prefer to prioritize their ideas by discussing and conducting tests in their own laboratories or clinical departments, and publishing research reports afterwards. However, there are instances when simple observations and research studies in a single center are not capable of explaining and testing new groundbreaking ideas. Formulating hypothesis articles first and calling for multicenter and interdisciplinary research can be a solution in such instances, potentially launching influential scientific directions, if not academic disciplines.

The aim of this article is to overview the importance and implications of infrequently published scientific hypotheses that may open new avenues of thinking and research.

Despite the seemingly established views on innovative ideas and hypotheses as essential research tools, no structured definition exists to tag the term and systematically track related articles. In 1973, the Medical Subject Heading (MeSH) of the U.S. National Library of Medicine introduced “Research Design” as a structured keyword that referred to the importance of collecting data and properly testing hypotheses, and indirectly linked the term to ethics, methods and standards, among many other subheadings.

One of the experts in the field defines “hypothesis” as a well-argued analysis of available evidence to provide a realistic (scientific) explanation of existing facts, fill gaps in public understanding of sophisticated processes, and propose a new theory or a test. 4 A hypothesis can be proven wrong partially or entirely. However, even such an erroneous hypothesis may influence progress in science by initiating professional debates that help generate more realistic ideas. The main ethical requirement for hypothesis authors is to be honest about the limitations of their suggestions. 5

EXAMPLES OF INFLUENTIAL SCIENTIFIC HYPOTHESES

Daily routine in a research laboratory may lead to groundbreaking discoveries provided the daily accounts are comprehensively analyzed and reproduced by peers. The discovery of penicillin by Sir Alexander Fleming (1928) can be viewed as a prime example of such discoveries that introduced therapies to treat staphylococcal and streptococcal infections and modulate blood coagulation. 6 , 7 Penicillin got worldwide recognition due to the inventor's seminal works published by highly prestigious and widely visible British journals, effective ‘real-world’ antibiotic therapy of pneumonia and wounds during World War II, and euphoric media coverage. 8 In 1945, Fleming, Florey and Chain got a much deserved Nobel Prize in Physiology or Medicine for the discovery that led to the mass production of the wonder drug in the U.S. and ‘real-world practice’ that tested the use of penicillin. What remained globally unnoticed is that Zinaida Yermolyeva, the outstanding Soviet microbiologist, created the Soviet penicillin, which turned out to be more effective than the Anglo-American penicillin and entered mass production in 1943; that year marked the turning of the tide of the Great Patriotic War. 9 One of the reasons of the widely unnoticed discovery of Zinaida Yermolyeva is that her works were published exclusively by local Russian (Soviet) journals.

The past decades have been marked by an unprecedented growth of multicenter and global research studies involving hundreds and thousands of human subjects. This trend is shaped by an increasing number of reports on clinical trials and large cohort studies that create a strong evidence base for practice recommendations. Mega-studies may help generate and test large-scale hypotheses aiming to solve health issues globally. Properly designed epidemiological studies, for example, may introduce clarity to the hygiene hypothesis that was originally proposed by David Strachan in 1989. 10 David Strachan studied the epidemiology of hay fever in a cohort of 17,414 British children and concluded that declining family size and improved personal hygiene had reduced the chances of cross infections in families, resulting in epidemics of atopic disease in post-industrial Britain. Over the past four decades, several related hypotheses have been proposed to expand the potential role of symbiotic microorganisms and parasites in the development of human physiological immune responses early in life and protection from allergic and autoimmune diseases later on. 11 , 12 Given the popularity and the scientific importance of the hygiene hypothesis, it was introduced as a MeSH term in 2012. 13

Hypotheses can be proposed based on an analysis of recorded historic events that resulted in mass migrations and spreading of certain genetic diseases. As a prime example, familial Mediterranean fever (FMF), the prototype periodic fever syndrome, is believed to spread from Mesopotamia to the Mediterranean region and all over Europe due to migrations and religious prosecutions millennia ago. 14 Genetic mutations spearing mild clinical forms of FMF are hypothesized to emerge and persist in the Mediterranean region as protective factors against more serious infectious diseases, particularly tuberculosis, historically common in that part of the world. 15 The speculations over the advantages of carrying the MEditerranean FeVer (MEFV) gene are further strengthened by recorded low mortality rates from tuberculosis among FMF patients of different nationalities living in Tunisia in the first half of the 20th century. 16

Diagnostic hypotheses shedding light on peculiarities of diseases throughout the history of mankind can be formulated using artefacts, particularly historic paintings. 17 Such paintings may reveal joint deformities and disfigurements due to rheumatic diseases in individual subjects. A series of paintings with similar signs of pathological conditions interpreted in a historic context may uncover mysteries of epidemics of certain diseases, which is the case with Ruben's paintings depicting signs of rheumatic hands and making some doctors to believe that rheumatoid arthritis was common in Europe in the 16th and 17th century. 18

WRITING SCIENTIFIC HYPOTHESES

There are author instructions of a few journals that specifically guide how to structure, format, and make submissions categorized as hypotheses attractive. One of the examples is presented by Med Hypotheses , the flagship journal in its field with more than four decades of publishing and influencing hypothesis authors globally. However, such guidance is not based on widely discussed, implemented, and approved reporting standards, which are becoming mandatory for all scholarly journals.

Generating new ideas and scientific hypotheses is a sophisticated task since not all researchers and authors are skilled to plan, conduct, and interpret various research studies. Some experience with formulating focused research questions and strong working hypotheses of original research studies is definitely helpful for advancing critical appraisal skills. However, aspiring authors of scientific hypotheses may need something different, which is more related to discerning scientific facts, pooling homogenous data from primary research works, and synthesizing new information in a systematic way by analyzing similar sets of articles. To some extent, this activity is reminiscent of writing narrative and systematic reviews. As in the case of reviews, scientific hypotheses need to be formulated on the basis of comprehensive search strategies to retrieve all available studies on the topics of interest and then synthesize new information selectively referring to the most relevant items. One of the main differences between scientific hypothesis and review articles relates to the volume of supportive literature sources ( Table 1 ). In fact, hypothesis is usually formulated by referring to a few scientific facts or compelling evidence derived from a handful of literature sources. 19 By contrast, reviews require analyses of a large number of published documents retrieved from several well-organized and evidence-based databases in accordance with predefined search strategies. 20 , 21 , 22

The format of hypotheses, especially the implications part, may vary widely across disciplines. Clinicians may limit their suggestions to the clinical manifestations of diseases, outcomes, and management strategies. Basic and laboratory scientists analysing genetic, molecular, and biochemical mechanisms may need to view beyond the frames of their narrow fields and predict social and population-based implications of the proposed ideas. 23

Advanced writing skills are essential for presenting an interesting theoretical article which appeals to the global readership. Merely listing opposing facts and ideas, without proper interpretation and analysis, may distract the experienced readers. The essence of a great hypothesis is a story behind the scientific facts and evidence-based data.

ETHICAL IMPLICATIONS

The authors of hypotheses substantiate their arguments by referring to and discerning rational points from published articles that might be overlooked by others. Their arguments may contradict the established theories and practices, and pose global ethical issues, particularly when more or less efficient medical technologies and public health interventions are devalued. The ethical issues may arise primarily because of the careless references to articles with low priorities, inadequate and apparently unethical methodologies, and concealed reporting of negative results. 24 , 25

Misinterpretation and misunderstanding of the published ideas and scientific hypotheses may complicate the issue further. For example, Alexander Fleming, whose innovative ideas of penicillin use to kill susceptible bacteria saved millions of lives, warned of the consequences of uncontrolled prescription of the drug. The issue of antibiotic resistance had emerged within the first ten years of penicillin use on a global scale due to the overprescription that affected the efficacy of antibiotic therapies, with undesirable consequences for millions. 26

The misunderstanding of the hygiene hypothesis that primarily aimed to shed light on the role of the microbiome in allergic and autoimmune diseases resulted in decline of public confidence in hygiene with dire societal implications, forcing some experts to abandon the original idea. 27 , 28 Although that hypothesis is unrelated to the issue of vaccinations, the public misunderstanding has resulted in decline of vaccinations at a time of upsurge of old and new infections.

A number of ethical issues are posed by the denial of the viral (human immunodeficiency viruses; HIV) hypothesis of acquired Immune deficiency Syndrome (AIDS) by Peter Duesberg, who overviewed the links between illicit recreational drugs and antiretroviral therapies with AIDS and refuted the etiological role of HIV. 29 That controversial hypothesis was rejected by several journals, but was eventually published without external peer review at Med Hypotheses in 2010. The publication itself raised concerns of the unconventional editorial policy of the journal, causing major perturbations and more scrutinized publishing policies by journals processing hypotheses.

WHERE TO PUBLISH HYPOTHESES

Although scientific authors are currently well informed and equipped with search tools to draft evidence-based hypotheses, there are still limited quality publication outlets calling for related articles. The journal editors may be hesitant to publish articles that do not adhere to any research reporting guidelines and open gates for harsh criticism of unconventional and untested ideas. Occasionally, the editors opting for open-access publishing and upgrading their ethics regulations launch a section to selectively publish scientific hypotheses attractive to the experienced readers. 30 However, the absence of approved standards for this article type, particularly no mandate for outlining potential ethical implications, may lead to publication of potentially harmful ideas in an attractive format.

A suggestion of simultaneously publishing multiple or alternative hypotheses to balance the reader views and feedback is a potential solution for the mainstream scholarly journals. 31 However, that option alone is hardly applicable to emerging journals with unconventional quality checks and peer review, accumulating papers with multiple rejections by established journals.

A large group of experts view hypotheses with improbable and controversial ideas publishable after formal editorial (in-house) checks to preserve the authors' genuine ideas and avoid conservative amendments imposed by external peer reviewers. 32 That approach may be acceptable for established publishers with large teams of experienced editors. However, the same approach can lead to dire consequences if employed by nonselective start-up, open-access journals processing all types of articles and primarily accepting those with charged publication fees. 33 In fact, pseudoscientific ideas arguing Newton's and Einstein's seminal works or those denying climate change that are hardly testable have already found their niche in substandard electronic journals with soft or nonexistent peer review. 34

CITATIONS AND SOCIAL MEDIA ATTENTION

The available preliminary evidence points to the attractiveness of hypothesis articles for readers, particularly those from research-intensive countries who actively download related documents. 35 However, citations of such articles are disproportionately low. Only a small proportion of top-downloaded hypotheses (13%) in the highly prestigious Med Hypotheses receive on average 5 citations per article within a two-year window. 36

With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term. 36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances of citing and crediting authors of genuine research ideas.

A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989, 10 is still attracting numerous citations on Scopus, the largest bibliographic database. As of August 28, 2019, the number of the linked citations in the database is 3,201. Of the citing articles, 160 are cited at least 160 times ( h -index of this research topic = 160). The first three citations are recorded in 1992 and followed by a rapid annual increase in citation activity and a peak of 212 in 2015 ( Fig. 1 ). The top 5 sources of the citations are Clin Exp Allergy (n = 136), J Allergy Clin Immunol (n = 119), Allergy (n = 81), Pediatr Allergy Immunol (n = 69), and PLOS One (n = 44). The top 5 citing authors are leading experts in pediatrics and allergology Erika von Mutius (Munich, Germany, number of publications with the index citation = 30), Erika Isolauri (Turku, Finland, n = 27), Patrick G Holt (Subiaco, Australia, n = 25), David P. Strachan (London, UK, n = 23), and Bengt Björksten (Stockholm, Sweden, n = 22). The U.S. is the leading country in terms of citation activity with 809 related documents, followed by the UK (n = 494), Germany (n = 314), Australia (n = 211), and the Netherlands (n = 177). The largest proportion of citing documents are articles (n = 1,726, 54%), followed by reviews (n = 950, 29.7%), and book chapters (n = 213, 6.7%). The main subject areas of the citing items are medicine (n = 2,581, 51.7%), immunology and microbiology (n = 1,179, 23.6%), and biochemistry, genetics and molecular biology (n = 415, 8.3%).

Interestingly, a recent analysis of 111 publications related to Strachan's hygiene hypothesis, stating that the lack of exposure to infections in early life increases the risk of rhinitis, revealed a selection bias of 5,551 citations on Web of Science. 37 The articles supportive of the hypothesis were cited more than nonsupportive ones (odds ratio adjusted for study design, 2.2; 95% confidence interval, 1.6–3.1). A similar conclusion pointing to a citation bias distorting bibliometrics of hypotheses was reached by an earlier analysis of a citation network linked to the idea that β-amyloid, which is involved in the pathogenesis of Alzheimer disease, is produced by skeletal muscle of patients with inclusion body myositis. 38 The results of both studies are in line with the notion that ‘positive’ citations are more frequent in the field of biomedicine than ‘negative’ ones, and that citations to articles with proven hypotheses are too common. 39

Social media channels are playing an increasingly active role in the generation and evaluation of scientific hypotheses. In fact, publicly discussing research questions on platforms of news outlets, such as Reddit, may shape hypotheses on health-related issues of global importance, such as obesity. 40 Analyzing Twitter comments, researchers may reveal both potentially valuable ideas and unfounded claims that surround groundbreaking research ideas. 41 Social media activities, however, are unevenly distributed across different research topics, journals and countries, and these are not always objective professional reflections of the breakthroughs in science. 2 , 42

Scientific hypotheses are essential for progress in science and advances in healthcare. Innovative ideas should be based on a critical overview of related scientific facts and evidence-based data, often overlooked by others. To generate realistic hypothetical theories, the authors should comprehensively analyze the literature and suggest relevant and ethically sound design for future studies. They should also consider their hypotheses in the context of research and publication ethics norms acceptable for their target journals. The journal editors aiming to diversify their portfolio by maintaining and introducing hypotheses section are in a position to upgrade guidelines for related articles by pointing to general and specific analyses of the subject, preferred study designs to test hypotheses, and ethical implications. The latter is closely related to specifics of hypotheses. For example, editorial recommendations to outline benefits and risks of a new laboratory test or therapy may result in a more balanced article and minimize associated risks afterwards.

Not all scientific hypotheses have immediate positive effects. Some, if not most, are never tested in properly designed research studies and never cited in credible and indexed publication outlets. Hypotheses in specialized scientific fields, particularly those hardly understandable for nonexperts, lose their attractiveness for increasingly interdisciplinary audience. The authors' honest analysis of the benefits and limitations of their hypotheses and concerted efforts of all stakeholders in science communication to initiate public discussion on widely visible platforms and social media may reveal rational points and caveats of the new ideas.

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

Author Contributions:

• Conceptualization: Gasparyan AY, Yessirkepov M, Kitas GD.
• Methodology: Gasparyan AY, Mukanova U, Ayvazyan L.
• Writing - original draft: Gasparyan AY, Ayvazyan L, Yessirkepov M.
• Writing - review & editing: Gasparyan AY, Yessirkepov M, Mukanova U, Kitas GD.

A hypothesis (plural hypothesis) is a proposed clarification for a phenomenon. For a hypothesis to be logical speculation. These are the logical strategy necessitate that one can test it. Researchers for the most part base logical hypothesis on past perceptions that can’t sufficiently be clarified with the accessible logical hypothesis.

Despite the fact that the word “hypothesis” is regularly in use. Equivalently, a logical hypothesis isn’t equivalent to a scientific hypothesis. A working hypothesis is a temporarily acknowledged hypothesis proposed for additional exploration, in a cycle starting with an informed estimate or thought.

                                                                                             Hypothesis

In its antiquated utilization, hypothesis alluded to an outline of the plot of an old-style dramatization. The English word hypothesis comes from the antiquated Greek word hypothesis. Its exacting or etymological sense is “putting or setting under”. Henceforth in broad use has numerous different implications including “assumption”.

In Common Utilization

In common utilization, a hypothesis alludes to a temporary thought whose legitimacy requires assessment. For legitimate assessment, the composer of a hypothesis needs to characterize particulars in operational terms. A hypothesis requires more work by the scientist to either affirm or negate it. At the appointed time, an affirmed hypothesis may turn out to be important for a hypothesis. At times may develop to turn into a hypothesis itself.

Regularly, a logical hypothesis has the type of numerical model. Sometimes, however not generally, one can likewise plan them as existential proclamations. Expressing that some specific case of the phenomenon under assessment has some trademark and causal clarifications. This has the overall type of explanations, expressing that each case of the specific trademark.

In Innovative Science

In innovative science, a hypothesis is useful to define temporary thoughts inside a business setting. The figured hypothesis is then assessed where either the hypothesis is demonstrated to be “valid” or “bogus”. It is through an undeniable nature or falsifiability-arranged test.

Any valuable hypothesis will empower forecasts by thinking (counting deductive thinking). It may foresee the result of an analysis in a research centre setting or the perception of wonder in nature. The forecast may likewise conjure measurements and just discussion about probabilities. Karl Popper, following others, has contended that a hypothesis must be falsifiable. One can’t view a suggestion or hypothesis as logical on the off chance that it doesn’t concede the chance of being indicated bogus. Different thinkers of science have dismissed the model of falsifiability or enhanced it with other measures.

For example, undeniable nature for e.g., verificationism or soundness like affirmation comprehensive quality. The logical technique includes experimentation, to test the capacity of some hypothesis to satisfactorily address the inquiry under scrutiny. Conversely, liberated perception isn’t as liable to bring up unexplained issues or open issues in science. As it would the plan of a pivotal trial to test the hypothesis. A psychological test may likewise be utilized to test the hypothesis too.

In outlining a hypothesis, the examiner must not right now know the result of a test. It remains sensibly under proceeding with examination. Just in such cases does the analysis, test or study conceivably increment the likelihood of indicating the reality of a hypothesis.

If the specialist definitely knows the result, it considers an “outcome”. The scientist ought to have just thought about this while detailing the hypothesis. On the off chance that one can’t survey the expectations by perception or by experience. The hypothesis should be tried by others giving perceptions. For instance, another innovation or hypothesis may make the essential trials practical.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The theory ought to be clear and exact to believe it to be solid.
• If the hypothesis is a social theory, at that point it ought to express the connection between factors.
• The theory must be explicit and ought to have scope for leading more tests.
• The method of clarification of the theory must be basic and it should likewise be perceived that the straightforwardness of the hypothesis isn’t identified with its essentialness.

Sources of Hypothesis

Following are the sources of the hypothesis:

• The likeness between the wonder.
• Observations from past investigations, present-day encounters and from the contenders.
• Scientific hypothesis.
• General designs that impact the considering cycle individuals.

Types of Hypothesis

There are six forms of the hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a connection between one ward variable and a solitary autonomous variable. For instance, If you eat more vegetables, you will get in shape quicker. Here, eating more vegetables is a free factor, while getting more fit is the needy variable.

Complex Hypothesis

It shows the connection between at least two ward factors and at least two autonomous factors. Eating more vegetables and natural products prompts weight reduction. May be sparkling skin, diminishes the danger of numerous infections, for example, coronary illness, hypertension and a few diseases.

Directional Hypothesis

It shows how an analyst is scholarly and focused on a specific result. The connection between the factors can likewise foresee its inclination. For instance, kids matured four years eating appropriate food over a five-year time frame are having higher IQ levels than youngsters not having a legitimate dinner. This shows the impact and course of impact.

Non-directional Hypothesis

It is utilized when there is no theory included. It is an explanation that a relationship exists between two factors, without foreseeing the specific nature (course) of the relationship.

Null Hypothesis

It gives the explanation which is in opposition to the theory. It’s a negative assertion, and there is no connection between autonomous and subordinate factors. The image is indicated by “HO”.

Associative and Causal Hypothesis

Acquainted hypothesis happens when there is an adjustment in one variable bringing about an adjustment in the other variable. Though, the causal hypothesis proposes a circumstances and logical results connection between at least two factors.

Examples of Hypothesis

Following are the examples of the hypothesis according to their types:

• Consumption of sweet beverages consistently prompts weight is a case of a straightforward theory.
• All lilies have a similar number of petals is a case of an invalid hypothesis.
• If an individual gets 7 hours of rest, at that point he will feel less weakness than if he dozens less.

FAQs about Hypothesis

Q.1. Write a short note on the term hypothesis.

Answer: A hypothesis (plural hypothesis) is a proposed clarification for a phenomenon. For a hypothesis to be logical speculation. The logical strategy necessitates that one can test it. Researchers for the most part base logical hypothesis on past perceptions that can’t sufficiently be clarified with the accessible logical hypotheses. Despite the fact that the words “hypothesis” and “hypothesis” are regularly utilized equivalently, a logical hypothesis isn’t equivalent to a scientific hypothesis.

Q.2. What are the functions of the Hypothesis?

Answer: Following are the functions performed by the hypothesis:

• Hypothesis helps in mentioning an objective fact and tests conceivable.
• It turns into the beginning point for the formal examination.
• Hypothesis helps in checking the perceptions.
• It helps in coordinating the requests in the correct ways.

Q.3. How will Hypothesis help in Scientific Method?

Answer: Scientists use theory to put down their considerations coordinating how the test would happen. Following are the means that are engaged with the logical strategy:

• Formation of inquiry
• Doing foundation research
• Creation of hypothesis
• Designing an investigation
• Collection of information
• Result examination
• Summarizing the trial
• Communicating the outcomes

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• Published: 03 April 2024

Increases in pre-stimulus theta and alpha oscillations precede successful encoding of crossmodal associations

• Jan Ostrowski   ORCID: orcid.org/0000-0001-9928-272X 1 &
• Michael Rose   ORCID: orcid.org/0000-0002-9789-7066 1  

Scientific Reports volume  14 , Article number:  7895 ( 2024 ) Cite this article

Metrics details

• Cognitive neuroscience
• Human behaviour
• Long-term memory

A central aspect of episodic memory is the formation of associations between stimuli from different modalities. Current theoretical approaches assume a functional role of ongoing oscillatory power and phase in the theta band (3–7 Hz) for the encoding of crossmodal associations. Furthermore, ongoing activity in the theta range as well as alpha (8–12 Hz) and low beta activity (13–20 Hz) before the presentation of a stimulus is thought to modulate subsequent cognitive processing, including processes that are related to memory. In this study, we tested the hypothesis that pre-stimulus characteristics of low frequency activity are relevant for the successful formation of crossmodal memory. The experimental design that was used specifically allowed for the investigation of associative memory independent from individual item memory. Participants ( n  = 51) were required to memorize associations between audiovisual stimulus pairs and distinguish them from newly arranged ones consisting of the same single stimuli in the subsequent recognition task. Our results show significant differences in the state of pre-stimulus theta and alpha power between remembered and not remembered crossmodal associations, clearly relating increased power to successful recognition. These differences were positively correlated with memory performance, suggesting functional relevance for behavioral measures of associative memory. Further analysis revealed similar effects in the low beta frequency ranges, indicating the involvement of different pre-stimulus-related cognitive processes. Phase-based connectivity measures in the theta band did not differ between remembered and not remembered stimulus pairs. The findings support the assumed functional relevance of theta band oscillations for the formation of associative memory and demonstrate that an increase of theta as well as alpha band oscillations in the pre-stimulus period is beneficial for the establishment of crossmodal memory.

Introduction

Patterns of ongoing brain activity can modulate how subsequent stimuli are perceived and processed 1 , 2 , 3 , 4 . This pre-stimulus activity has been shown to also affect how information is encoded to long-term memory, subsequently affecting memory performance itself 5 , 6 , 7 , 8 . Furthermore, previous research suggests that oscillatory activity across a wide range of frequency bands might be involved in the process, including low-frequency oscillations 9 , 10 , as well as oscillations as high as 55–70 Hz 11 . In terms of episodic memory, particularly spatial information is represented in the hippocampus through the firing of event or place cells, which is embedded in an ongoing theta rhythm of 3–7 Hz 12 , 13 . The information can be coded by the firing rate through the mechanism of specific receptive fields on the one hand, but also in the temporal domain by shifting the firing sequences along the phase of the overarching theta cycle (phase precession). However, these mechanisms are not only applicable to spatial information, but might also explain how associative information is encoded to long-term memory. As most of the content of episodic memory involves information integrated from different sensory modalities, it is all the more relevant that recent work extended the explanatory scope of rate and temporal coding, claiming that the same mechanism is used to form associations between discrete stimuli from different modalities and form relational networks between them, which ultimately serves episodic memory 14 . The authors suggest that event cells in the hippocampus coding for discrete events fire according to transient theta phase precession and subsequently lock onto the early theta phase. It can be assumed that the phase of theta oscillations may represent windows of short-term synaptic plasticity, and coordinate inputs from different sources. Thus, oscillatory pre-stimulus activity in the theta frequency band might be crucial for the formation of crossmodal associations in humans.

Specifically, evidence from non-invasive electroencephalography (EEG) studies suggests that increases in theta power before stimulus onset might be related to enhanced performance in tasks measuring episodic or long-term memory 10 . While some studies suggested that theta oscillations might be involved in item memory 15 , 16 , increased theta has also been associated specifically with better recollection of contextual information, where the association of stimuli to one of few contexts needed to be memorized 6 , 17 . Moreover, there is evidence suggesting that theta oscillations may play a role not only in the binding of stimuli to contexts but also in the encoding of individual associations between two stimuli 18 . In that study, participants were required to memorize individual word pairs, and were later cued by one of the words and instructed to verbalize the other half of the pair. The authors report significant increases in theta power, specifically in the pre-stimulus interval. This line of evidence gains additional support by studies utilizing intracranial EEG (iEEG), showing that the hippocampus displays increased theta oscillations associated with better memory performance 19 . However, it is still unclear whether pre-stimulus theta power is involved in modulating the encoding of individual associations between stimuli from different sensory modalities.

In this context, insights from research using animal models might provide a framework to investigate the role of theta amplitude and phase characteristics in the encoding of crossmodal associations. Supporting evidence was reported by Terada and colleagues 14 , who trained rats to perform a cue-combination task that required the integration and subsequent association of sequentially presented sound and odor stimuli. The results suggest that the firing of hippocampal neurons might represent associations between multimodal stimuli, with phase information from the theta band serving as a marker for the temporal order of discrete events. However, evidence in humans has been scarce. In one recent study, Clouter and colleagues presented participants with a multimodal memory task, in which they were required to memorize the association between a movie clip and a sound 20 . Both stimuli were presented simultaneously during encoding, but the authors manipulated the synchrony between them by fluctuating the luminance of the video and the amplitude of the sound according to a 4 Hz sine wave, while also varying their phase offset. The results show that memory performance was best when no phase offset was introduced, and that the effect was specific to the theta band. Another study followed up on these results and showed that even the single-trial phase synchrony between visual and auditory cortices, whose activity was entrained by 4 Hz fluctuations of the stimuli, could predict success in the formation of associations between different sensory information for long-term memory 21 . Thus, in addition to studies that showed a relevance of theta band amplitude modulation, the latter studies revealed an important role of phase coupling for the encoding of material from different modalities. The presented evidence further suggests that successful encoding of crossmodal associations may rely on elevated functional connectivity between the corresponding sensory areas, and that the connectivity might be centered on rhythmic components within the range of theta oscillations.

While theta oscillations seem to play a crucial role before and during the encoding of complex information to memory, there is also evidence on the involvement of oscillations in the alpha (8–12 Hz) and beta frequency bands (13–30 Hz). Alpha oscillations have been theorized to play a crucial role in selective attention 22 , which was suggested to aid encoding by inhibiting distracting information 9 , 23 . Investigations using iEEG measurements support this idea as increases in medial temporal pre-stimulus alpha power were reported to be associated with better memory performance 19 . Similarly, beta band activity has been observed to benefit memory encoding via inhibitory processes 24 . In the context of crossmodal associations, Scholz and colleagues reported that pre-stimulus beta oscillations in the lower bands (13–17 Hz) were indicative of successful encoding of audiovisual source memory 6 . However, it remains unclear how ongoing alpha and beta oscillations might contribute to the formation of individual crossmodal associations.

In the present study, we aimed to directly assess the relevance of pre-stimulus amplitude and phase characteristics for the formation of individual crossmodal associations between visual and auditory stimuli. In particular, pre-stimulus theta, alpha and low beta band activity, i.e. activity before the encoding of multimodal stimuli, might play a functional role in subsequent memory performance. We additionally examined oscillatory effects regarding post-stimulus processing as well as effects during memory retrieval. In terms of phase characteristics, phase coupling in the theta band can be hypothesized to be important during the processing of the stimuli for binding crossmodal information. Thus, if phase-based connectivity contributes to the encoding of audiovisual associative information, differences should be observed in the connectivity between visual and auditory areas. Specifically, successful memory formation would be accompanied by increased phase-based connectivity between auditory and visual areas, as compared to unsuccessful memory formation during the processing of the stimuli. Thus, we analyzed the phase-based connectivity between occipital electrodes (image-related activity) 25 and frontocentral electrodes (sound-related activity) 26 , 27 .

We employed a Subsequent Memory Effects (SME) task, which is an established experimental design to investigate mechanisms related to the encoding of information to (episodic) memory. This paradigm has been used in a variety of modalities, including EEG 5 , 6 , 28 , 29 , magnetoencephalogram (MEG) 10 , 30 , and functional imaging 31 , 32 . The majority of the studies investigating SMEs, however, focused on the encoding of individual associations within one single sensory modality 5 , 17 , 33 , 34 , 35 . We modified the unimodal design to allow for individual crossmodal associations to be encoded. Participants were required to memorize associations between images and sounds while brain activity was recorded via EEG. One experimental run consisted of an encoding phase, a short distraction task, and a subsequent testing phase in the form of cued recognition. During encoding, semantically unrelated real-life images and sounds were presented simultaneously after a cue. Participants were instructed to indicate whether both individual stimuli were animal-related while making an effort to memorize the stimulus pair as a whole. After the distraction task, which required the participants to count backwards for several minutes, they were presented with the same stimulus pairs as during encoding, as well as the same number of new pairs consisting of the same individual images and sounds but randomly shuffled for new combinations. Participants needed to indicate whether they remembered the pair from the previous encoding phase or not. The stimulus pairs always consisted of individual stimuli that were presented during encoding and only the pairing was identical or different. Therefore, this task design enabled us to specifically target memory performance in terms of associations rather than individual stimuli.

Participants

In total, 55 healthy participants were recruited for this study. We had to exclude the data from four participants because of too many missing trials (1), low data quality (1), and hardware problems during data acquisition (2). This resulted in a final sample of N  = 51 participants (64.71% female) that could be used for analysis, with a mean age of 24.41 years (SD = 3.82), ranging from 18 to 34 years. Participants had normal or corrected-to-normal vision and hearing ability. All participants gave their informed consent and received financial reimbursement for taking part in the study. This investigation was approved by the ethics committee of the Hamburg Medical Council (PV5893). We confirm that all experiments were performed in accordance with relevant guidelines and regulations.

Task and procedure

For this study, we implemented a Subsequent Memory Effects (SME) task consisting of three experimental runs. Each run consisted of an encoding phase, a short intermission, and a subsequent recognition phase (see Fig.  1 for a schematic overview of one experimental run). As we wanted to measure crossmodal memory, pairs consisting of one image and one sound were randomly selected from an internal stimulus database. The images were shown with a resolution of 640 × 480 pixels, a 24-bit color depth, and depicted photographs of natural or man-made scenes. A white fixation cross was layered over every image. The sounds were real-life recordings of either sounds and noises from nature (e.g. animal calls) or from a man-made environment (e.g. a honk from a car). All sounds were cropped to a duration of 2 s, and featured a bit rate of 1411 kBit/s. The pairs were pulled in a manner so that the stimulus material was unique in each run and did not repeat between runs. Specifically, each pair and individual stimulus occurred only once across the three encoding phases from the three experimental runs. Furthermore, images were paired with sounds so that congruency effects within pairs were avoided 36 . For example, while the image of a wolf could not have been paired with the sound of a wolf howl, it could have been paired with the sound of bird call or a honking car, as this would not constitute semantic congruence.

Schematic overview of one experimental run of the SME task. The run consists of an encoding phase, in which image-sound pairs needed to be memorized. This was followed by a short distraction task, where participants were required to count down in steps of 7/9/13 from 100/115/125. Subsequently, old and new pairs consisting of the same individual stimuli presented during encoding were shown, and participants indicated whether they remembered the particular pairing or not.

The encoding phase of each run consisted of 47 trials in which the audiovisual pairs were presented simultaneously for 2 s. The stimulus pairs were preceded by a red fixation cross with a duration of 2 s. After stimulus offset, the white fixation cross remained for a fixed duration of 1 s, which was followed by a variable inter-trial interval of 2 to 4 s. For every encoding trial, the participants were instructed to memorize the stimulus pairs, and to indicate with a button press whether both image and sound represented an animal (right mouse button) or not (left mouse button). Button presses were registered as a response during the 2 s of stimulus presentation and subsequent 1 s (Fig.  1 ) but were otherwise counted as a missed response. The encoding phase was followed by a brief intermission of approximately 3 min, in which the participants were asked to count down aloud from 100 (115 and 125 in the second and third run, respectively) in steps of 7 (9 and 13 in the second and third run, respectively).

In the subsequent recognition phase, the 47 audiovisual pairings from the preceding encoding phase were presented again. In addition, 47 new pairings were shown that were created from the individual images and sounds from previously learned pairs. In the instruction, participants were explicitly informed about the nature of new pairings and were further told that no new images and sounds would be introduced. All stimulus pairs were presented for 2 s, with a white fixation cross in the middle of the screen during stimulus presentation and for a fixed duration of 1 s directly after. During these 3s, association recognition was tested, as the participants were asked to indicate via button-press whether the current pair had already been presented in the preceding encoding phase (left mouse button) or not (right mouse button). The participants were encouraged to give a positive response only when confident, and to give a negative response when in doubt, to reduce the risk of false positive responses. The subsequent inter-trial interval was set to 4 s, during which a white fixation cross was visible. Across three experimental runs, participants were presented with 141 unique encoding trials and 282 recognition trials in total.

Analysis of behavioral data

The behavioral performance of the categorization task during encoding was assessed by computing the average percentage of trials in which participants correctly identified whether both the visual and auditory stimulus represented an animal. In the recognition phase, we extracted the percentages of correctly remembered old pairings (hit), not remembered old pairings (misses), correctly rejected new pairings (correct rejections), and seemingly remembered new pairings (false alarm). As a measure of sensitivity and memory performance, d’ was computed by calculating the difference between the z-transformed hit and false alarm rates. Then, the subject-specific d’ values were submitted to a one-sample t -test to investigate the likelihood of memories of stimulus pairs being formed across the sample. Furthermore, a two-factorial repeated-measures ANOVA was used to analyze reaction times during the recognition task, with the first factor type of pairing (old vs. new) and the second factor type of response (correct vs. incorrect).

Analysis of EEG data

Data acquisition.

After giving informed consent and filling out a short demographic questionnaire, participants were seated in an electrically shielded and sound-attenuated chamber. We used a 60-channel electrode setup (ActiCap, BrainProducts, Gilching, Germany) to record EEG, whereas four additional electrodes were placed on the left and right temple, as well as above and below the left eye, to record vertical and horizontal EOG. The signal was referenced online to FCz and re-referenced offline to a common average. The ground electrode was placed on the neck below Oz , and electrode impedances were kept below 15 kΩ. The signal was amplified with a low cut-off frequency of 0.53 (0.3 s time constant) and recorded at a sampling rate of 500 Hz. EEG activity was recorded during all encoding and recognition phases, but not during intermissions.

Preprocessing and time–frequency decomposition for power analysis

The offline preprocessing of the acquired EEG data was done using the Fieldtrip toolbox 37 for MATLAB (Release 2021a, The MathWorks Inc., Natick, Massachusetts, USA). For every trial, epochs were extracted from -1.5 s up until 2.5 s relative to stimulus onset. We used a high-pass filter at 0.5 Hz as implemented in Fieldtrip to filter out extreme low-frequency fluctuations. The data was then visually inspected and trials containing artifacts, such as high-frequency noise indicating muscular activity or spikes reminiscent of bad electrode connection, were removed. Independent Component Analysis (ICA) was used to identify components corresponding to eye blinks and lateral eye movements, which were then removed from the data. Per participant, 3.73 (SD = 1.8) components were removed on average, with most of them corresponding to muscle-related (M = 2.06, SD = 1.7) and blink artifacts (M = 1.02, SD = 0.32). Then, the data was again visually inspected, and trials that were still containing artifacts were removed. Finally, trials were separated into REM and NOTREM groups based on the responses from the corresponding recognition phase. A trial was considered as a REM trial if two conditions were met: First, the stimulus needed to be an “old” pair from the encoding phase, and the participant should have indicated that they remembered this pair. Second, the participant should have correctly identified a shuffled pair as new that contained the image shown in the original old pair. Conversely, a trial was considered a NOTREM trial if an old pair was not recognized. On average, 7.91% (SD = 7.61%) and 7.96% (SD = 7.96%) of trials were removed from the encoding data in the REM and NOTREM condition, respectively, resulting in an average of 59.29 (SD = 18.74) REM and 53.53 (SD = 18.07) NOTREM trials per participant after accounting for trials with missing responses. Bad channels were identified in the initial visual inspection, removed from the data, and interpolated using the weighted average from the neighboring channels after the ICA. Only one channel from one individual data set was interpolated in the course of the analysis.

Data from the recognition phase was preprocessed in the same manner as the encoding data. On average, 5.65 (SD = 3.14) independent components were removed from the data, the majority of which relating to blinks (M = 1.02, SD = 0.24) and muscular artifacts (M = 3.39, SD = 2.74). Here, a trial was categorized as REM if an “old” pair was presented and the participant recognized it correctly. If an “old” pair was presented and the participant did not remember it, it was categorized as a NOTREM trial. During preprocessing, 6.34% of trials with a correctly remembered stimulus pair were removed, while 6.27% of trials with not remembered stimuli were removed. This resulted in an average of 70 and 54 trials per participant, respectively.

Time–frequency decomposition was conducted in the frequency range of 1 to 40 Hz, with frequency bins of 1 Hz, and for the time interval of − 1 s to 2 s relative to stimulus onset. We chose the mtmconvol method for convolution as implemented in Fieldtrip 37 with a sliding Hanning window of a fixed length of 500 ms and a step size of 100 ms. This method is a computationally more efficient version of a convolution with a complex wavelet, where the wavelet itself is constructed by convoluting the real and imaginary sine component at each frequency with the tapering function. The data and the tapered wavelet are then Fourier-transformed and element-wise multiplied in the frequency domain. At the end, the inverse Fourier transform of the result is computed. The additional 500 ms of data before and after the time interval of interest extracted during preprocessing served as padding to avoid edge artifacts from the time–frequency decomposition. For both conditions (REM, NOTREM), the resulting power values were averaged across trials for each participant. No baseline correction was applied since we were primarily interested in within-subjects differences of oscillatory power between the REM and NOTREM conditions. Furthermore, the experimental design did not allow for a suitable baseline period, as encoding-related processes could not be ruled out during the inter-trial interval. The same procedure was applied for encoding as well as recognition data.

• Phase-based connectivity

As the theta band is thought to be involved in the process of binding incoming information 20 , we investigated whether memory performance with crossmodal stimuli could be differentiated by measures of phase-based functional connectivity. Thus, cross-spectral density data from all electrode combination pairs were extracted for the theta frequency range (3–7 Hz) in bins of 1 Hz and − 1 s to 0.5 s relative to stimulus onset in steps of 100 ms for single trials from all subjects. Next, functional connectivity was estimated using the weighted Phase Lag Index (wPLI 38 ), which utilizes the imaginary part of cross-spectral densities to compute the measure and is a non-directional marker of phase-based connectivity. To avoid positive bias, we used a squared estimated of wPLI as implemented in Fieldtrip 37 .

Statistical analysis of EEG data

In this study, we focused on the analysis of the time–frequency EEG data acquired during the encoding phase of the experiment. Based on previous research, the main analysis focused on potential SMEs for the pre-stimulus time interval in the theta, alpha, and beta frequency band (3–30 Hz). For that purpose, we used a non-parametric permutation testing approach with cluster-based correction for multiple comparisons as implemented in Fieldtrip 37 to statistically compare time–frequency data corresponding to REM trials to data from NOTREM trials from the encoding phase. To compare the specificity of the assumed relevance of theta band activity, the statistical analysis was calculated for the frequency spectrum of 1 to 40 Hz and a time window of − 1 to 2 s relative to stimulus onset. In this approach, paired-samples t- tests were conducted for every channel-time–frequency data point across participants between the REM and NOTREM condition. Adjacent data points showing significant differences between conditions ( p  < 0.05) were clustered in sets based on temporal, spatial, and spectral criteria. The sum of statistical values within each cluster was taken as cluster-level statistic , and the maximum of cluster-level values was chosen as the main test statistic for the comparison of conditions. Next, the Monte Carlo method was used to create a distribution of t- values by creating a single data set containing all trials from both conditions and randomly partitioned it into two groups. Statistical comparisons between these artificially created conditions were again conducted on the level of individual data points, and a cluster-level main statistic was computed. The drawing procedure was repeated 2000 times. On every iteration, the maximum cluster-level statistics for positive and negative clusters were extracted to create the cluster-level null-hypothesis distribution. The final p- value for the comparison of conditions was computed by assessing the proportion of random partitions with a larger test statistic than the one from the observed data. This procedure was repeated for all clusters found in the data, generating a p- value for the condition comparison for every cluster.

Building on the results from the main analysis, a correlational analysis was performed to investigate whether the magnitude of differences in oscillatory power between REM and NOTREM trials scaled with memory performance. For each channel-time–frequency data point in the range of 1 to 40 Hz and − 1 s to 2 s relative to stimulus onset, the difference in oscillatory power between REM and NOTREM trials was calculated. We then correlated the difference values with the performance measure d’ across participants using Pearson’s correlation coefficient. To correct for multiple comparisons, the same cluster-based correction was applied to the data as described in the previous paragraph. Furthermore, we investigated the relationship between pre- and post-stimulus activity. For this purpose, we selected those data points in the theta band from the pre-stimulus (− 1 s to − 0.1 s before stimulus onset) and the post-stimulus interval (0.1 s to 2 s after stimulus onset) that showed a significant difference between the REM and NOTREM condition as suggested by the results of their statistical comparison. The same analysis was conducted for the alpha band separately. After calculating the mean difference values for the pre-stimulus and post-stimulus intervals of each participant, we used the Pearson correlation coefficient to correlate the resulting means. This process was repeated for all channels that displayed significant data points in the respective frequency bands during both pre-stimulus as well as post-stimulus intervals, resulting in correlation coefficients for each channel. The Bonferroni method was utilized to correct for multiple correlations and adjust the resulting p -values accordingly.

To test whether connectivity between visual and auditory areas is increased for REM trials as compared to NOTREM trials, we chose  O1, O2, Oz, PO7, PO3, POz, PO4, and PO8 as seed channels. For every seed channel, the connectivity data corresponding to frontocentral channels was extracted and submitted to cluster-based permutation testing, using paired-samples t -tests on the sample level. Frontocentral electrodes were chosen as follows: F1, F2, Fz, FC3, FC1, FC2, FC4, C3, C1, Cz, C2, C4 .

Behavioral results

In the categorization task during encoding, participants performed with an average accuracy of 92.48% (SD = 9.89%), indicating a sufficiently high compliance with the task. Reaction times from trials with later remembered and later not remembered stimulus pairs did not differ significantly, t (50) = 1.7711, p  = 0.0826, although participants responded slightly faster on NOTREM trials (M = 1415.9 ms, SD = 311 ms) than on REM trials (M = 1449.9 ms, SD = 332.5 ms). See Fig.  2 for a visualization of the behavioral results.

Behavioral results from the SME task. ( a ) Distribution of reaction times for the categorization task during encoding for REM and NOTREM trials across the whole experiment. ( b ) Distribution of relative number of trials ( left ) as well as reaction times ( right ) for each response category of the recognition task. For the violin plots, areas are normalized to equal within each figure. Point markers represent mean values for each participant. The horizontal line within the boxplots marks the median of the respective subset, while the notch around the median represents its 95% confidence interval. The upper and lower edge of the boxplot mark the third and first quartile of the data, respectively. The legend only refers to ( b ). CR correct rejection, FA false alarm.

During the recognition phase, participants had to indicate whether the presented stimulus pair had already been shown during encoding or whether it was a new pair. The hit rate was defined as the percentage of trials in which old pairs were correctly identified as known, whereas the false-positive rate was defined as the percentage of trials in which new pairs were incorrectly identified as old. On average, the participants achieved a hit rate of 52.98% (SD = 13.63%) and a false-positive rate of 14.27% (SD = 8.66%). Responses were not recorded on an average of 5.08% of trials. We calculated d’ as a sensitivity measure for recognition performance, yielding a mean value of d’  = 1.2456 across the sample. The results from a one-sided t- test revealed that the mean d’ value was significantly different from 0, t (50) = 15.057, p  < 0.001, indicating that the recognition performance was above chance across participants.

We analyzed the reaction times from the recognition phase using a repeated-measures ANOVA with the factors type of pairing (old vs. new) and type of response (REM/correct rejection vs NOTREM/false alarm). Main effects of type of pairing , F (1,50) = 10.9464, p  < 0.01, as well as type of response were found, F (1,50) = 108.0863, p  < 0.001. Furthermore, the interaction between these factors was also found to be significant, F (1,50) = 9.2168, p  < 0.01. Thus, the correct recall of previously shown stimulus pairs and the correct identification of new stimulus pairs as new was accompanied by faster reaction times. In contrast, participants tended to respond slower in trials where old pairs were not remembered, as well as in trials where new pairs were falsely categorized as old. However, the difference in reaction times between levels of type of response (REM/correct rejection vs NOTREM/false alarm) was larger for new pairs (1604.4 ms vs. 1872.1 ms, p  < 0.001) than for old ones (1621.2 ms vs. 1769.6 ms, p  < 0.01; see Fig.  2 c).

Oscillatory results

Oscillatory power before and during encoding.

To assess whether oscillations before and during encoding differentiate between successful and unsuccessful memory formation, we analyzed the differences in power between REM and NOTREM trials for the corresponding time interval. The statistical comparison was conducted for the time interval of − 1 s to 2 s relative to stimulus onset, and for a frequency range of 1 Hz to 40 Hz. The analysis revealed a significant difference in oscillatory power between REM and NOTREM trials before and during the encoding of crossmodal associations ( p  < 0.05). Using a cluster-based permutation approach, a significant cluster was found in the pre-stimulus interval ranging from 1 to 18 Hz, suggesting higher oscillatory power for REM trials as compared to NOTREM trials (see Fig.  3 ). Similarly, increased power during REM trials was also observed during early encoding up to 0.9 s relative to stimulus onset in a frequency range of 1 to 27 Hz. Moreover, the analysis revealed an inverted effect in the late post-stimulus between 1 and 2 s after stimulus onset, spanning from 9 to 34 Hz, showing a negative cluster that did not extend into the theta band.

Subsequent memory effects on time–frequency power before and during encoding. Each row corresponds to one of the three distinct clusters observed in the data. ( a ) Time–frequency plots showing the results of the statistical comparison of REM – NOTREM. The vertical dashed line marks the stimulus onset. Positive t -values signify greater power for REM trials than NOTREM trials. Opaque data points show a significant difference at p  < 0.5. Each plot shows one of the three distinct clusters, with t- values averaged over the respective electrodes that are part of the cluster: Cluster 1 ( top ) averaged over all electrodes; Cluster 2 ( middle ) averaged over all electrodes except F7, FT7, and T7 ; Cluster 3 ( bottom ) averaged over all electrodes except AF4, AF8, C6, CP4, CP6, F6, F8, FC6, FT8, Fp2, Oz, P4, P6, P8, PO8, T8, TP8 . ( b ) Topographical distributions of t-values within each cluster. The columns display the distributions for the respective frequency band. Channels that are part of the respective cluster are marked in green, while yellow markers show the electrodes with maximum effect.

In the pre-stimulus theta range (3–7 Hz), the differences resulted to be most pronounced over the parietal as well as central areas of the right hemisphere, as well as over frontal-midline areas. The strongest effect was found at electrode P4 , showing the highest number of data points in the theta range with a statistically significant difference between REM and NOREM trials (Fig.  4 ). These results indicate that theta power before stimulus onset might be beneficial for the successful encoding of crossmodal associative information. For alpha band oscillations (8–12 Hz), the effect appeared to be most pronounced right before stimulus onset within the lower frequencies of the frequency band. Here, the effects are centered on left temporal as well as right frontal cortical areas, with the maximum effect at location FC6 . In terms of effects in the beta band (13–30 Hz), the pre-stimulus cluster incorporated only the lower frequencies between 13 and 18 Hz. In this frequency range, the largest effect was observed over left parietal and right frontal areas, most notably at electrode P3.

Power time courses for channels with maximum effects. The plot depicts power time courses for single electrodes averaged over the respective frequency bands. Columns denote the frequency range across which oscillatory power was averaged, whereas rows correspond to the three distinct clusters found in the statistical comparison of REM and NOTREM trials (row 1: pre-stimulus cluster; row 2: early post-stimulus cluster; row 3: late pre-stimulus cluster). The shadings around the lines mark the corrected standard error of means across participants 39 . Grey areas mark the time interval for which the difference between REM and NOTREM trials was significant in the respective cluster. The vertical dashed line marks the onset of the audiovisual stimulus pairs.

The analysis further revealed a positive cluster in the early post-stimulus interval during encoding. The differences in theta power were most pronounced in central parietal regions as well as frontal areas in the right hemisphere. The strongest effect was observed at electrode F6 (see Fig.  4 ). The results indicate that higher theta power during encoding might be positively related to the formation of crossmodal associations. Similar to the pre-stimulus interval, the early post-stimulus cluster spans also the alpha as well as the beta range up to 27 Hz. The maximum effect in the alpha range for this time interval was found at electrode C1 , while electrode C2 showed largest effect in the beta band. In the late post-stimulus cluster, however, where REM trials displayed significantly lower oscillatory power as compared to NOTREM trials, differences in beta band activity comprised most of the cluster. Here, the effect was most notable at location FC5 .

In a next step, we investigated whether memory performance measured by the sensitivity index d’ scales with the differences in oscillatory power between REM and NOTREM trials. Memory performance was correlated with the power differences for the same time–frequency range (1–40 Hz, − 1 s to 2 s relative to stimulus onset) and corrected for multiple comparisons. The analysis revealed a positive cluster spanning a frequency range of 4–15 Hz and a time interval of − 1 s to 1 s relative to stimulus onset ( p  < 0.05), indicating that greater differences between oscillatory power from REM and NOTREM trials tend to be accompanied by increased memory performance (Fig.  5 a,b). For the pre-stimulus interval, the maximum correlation was observed in left parietal and right anterior frontal areas for both the alpha (8–12 Hz) and the theta band (5–7 Hz). Conversely, frontal midline areas showed the highest correlation in the theta band after stimulus onset, while for the alpha band the effect was centered around left anterior frontal and central locations. Furthermore, we were interested in the relationship between REM – NOTREM power differences before and after stimulus onset in the encoding phase for the theta band. A correlational analysis was conducted to estimate the association of power differences in the theta band between pre-stimulus and post-stimulus time intervals. After correcting for multiple comparisons, we observed a significant positive correlation at location FC6 , r (49) = 0.48, p  < 0.05 (Fig.  5 c), indicating that greater pre-stimulus REM-NOTREM power differences coincided with greater post-stimulus differences. When conducting the same analysis in the alpha band, a significant correlation was found at electrode FC2 , r (49) = 0.48, p  < 0.05.

Correlation of pre-stimulus SME magnitude with memory performance and post-stimulus SME. ( a ) Time–frequency plot depicting the results of point-wise correlation of SME magnitude and memory performance measured as d’ . Data points show individual correlation coefficients, while the opaque data points mark the significant cluster ( p  < 0.05). Positive values signify a positive correlation. The stimulus onset is marked by a vertical dashed line. ( b ) Topographical distribution of correlation coefficients averaged over the pre-stimulus ( left ) and post-stimulus interval ( right ) for the alpha ( top ) and theta band ( bottom ). Channels that are part of the cluster in this time–frequency range are marked in green. ( c ) Relationship between pre-stimulus and post-stimulus SME magnitude for theta and alpha oscillations. For each frequency band, the topographical distribution of correlation coefficients is shown ( left ). The channels with a statistically significant correlation after correcting for multiple comparisons are marked in yellow. Scatter plots show the detailed correlation for the channels with the largest effect.

A possible confound in the analysis of pre-stimulus oscillations could arise from encoding-related activity spilling over across the inter-trial interval and influencing subsequent pre-stimulus activity. To control for that, we investigated whether the duration of the inter-trial interval could reliably predict pre-stimulus theta power on a single-trial basis by using a linear regression approach. Average pre-stimulus theta power was extracted for every REM and NOTREM trial for every participant and was used as the response variable for the model. Only data points that showed significant differences in oscillatory power between REM and NOTREM in cluster-based permutation testing were selected for averaging. Inter-trial duration was submitted as a continuous predictor, while the subsequent memory performance (REM or NOTREM) was used as a binomial predictor. The model differed significantly from a constant model, F (5664) = 12.4, p  < 0.001, but the effect was exclusively driven by the predictor for memory performance, t  =  − 4.68, p  < 0.001. In contrast, inter-trial interval duration did not predict pre-stimulus theta-power on a single-trial basis, t  =  − 1.62, p  = 0.104, indicating that a confound based on oscillatory activity from preceding trials is unlikely.

Phase-based functional connectivity before and during encoding

Cluster-based permutation testing was used to conduct a statistical comparison of phase-based connectivity measures between REM and NOTREM trials to investigate whether visual and auditory areas display increased connectivity in REM trials as compared to NOTREM trials. Statistical estimates were obtained for every time–frequency datapoint from the combinations of seed electrodes to frontocentral electrodes. For all seed electrodes, no significant clusters were found in the data, suggesting that phase-based connectivity between frontocentral and occipital areas did not differ between REM and NOTREM trials. However, on a descriptive level, increased connectivity between occipital and frontocentral sites could be observed for trials with remembered stimuli.

Oscillatory power during memory retrieval

Next, we investigated whether the effects found in the time interval before encoding could also be found before memory retrieval. We used cluster-based permutation analysis to compare oscillatory power between trials where old stimuli were correctly remembered and trials where old stimuli were categorized as not known. No statistically significant difference in the theta band was found in the pre-stimulus interval. Furthermore, we report no difference in alpha or beta power for the pre-stimulus interval in that analysis. However, results show one large negative cluster in the post-stimulus interval, stretching from 0.3 s to 2 s after stimulus onset, and ranging from 1 to 34 Hz across all channels (Fig.  6 ). This indicates that old stimuli which were correctly remembered were associated with lower theta as well as alpha and beta power during memory retrieval as compared to old stimuli that were not remembered. The effect in the theta band is mainly driven by activity in fronto-temporal and lateral-central areas measured 1.2 s to 2 s after stimulus onset, as well as lateral parietal regions. Notably, the maximum effect was observed at electrode P6 . For the alpha band, the effect peaked at electrode P7 , while P5 showed the maximum difference in the beta band. However, the onsets of the alpha and beta band effects were found to be at 0.3 s and 0.5 s after stimulus onset, respectively.

Memory effects on time–frequency power before and during recognition. Depicted are the results of statistically comparing oscillatory power between hit trials (old pairs correctly remembered) and miss trials (old pairs not remembered) from the recognition phase. The time–frequency plot ( left ) shows the t -values for every time–frequency data point, averaged over all electrodes. Opaque data points mark the extent of the negative cluster ( p  < 0.05). The stimulus onset is marked by the vertical dashed line. The topographies ( right ) show the topographical distribution of t -values averaged over the respective time intervals and frequency bands. Channels that are part of the cluster in this range are marked in green. The yellow circle marks the electrode with largest effect in the statistical comparison.

The present study investigated the involvement of pre-stimulus oscillatory activity and coupling in the formation of crossmodal associative memory by employing a multimodal Subsequent Memory Effects (SME) paradigm. Participants were required to memorize the association between simultaneously presented images and sounds, and the success of encoding was tested in a subsequent recognition test. We examined the differences in oscillatory power as well as phase-based functional connectivity in the pre-stimulus time interval. Notably, our analyses revealed significant differences in pre-stimulus power in the theta frequency range (3–7 Hz): trials with later remembered stimulus pairs (REM) were accompanied by increased theta as compared to trials with later not remembered stimuli (NOTREM) in the time period directly preceding the stimulus presentation. Similar differences were observed in the alpha (8–12 Hz) as well as the low beta range (13–18 Hz). Interestingly, the magnitude of SMEs in the theta and alpha band positively scaled with memory performance, suggesting a linear relationship between pre-stimulus increases in theta-alpha power and the ability to encode multimodal associations. However, our findings could not support our hypothesis regarding functional connectivity, as the modulation of phase-related connectivity in the theta band did not show a pronounced difference between remembered and not-remembered stimulus pairs. Thus, the present results point towards a memory-related function of theta band power but not phase-based connectivity. Additionally, oscillatory power before and during recognition was compared between REM and NOTREM trials, revealing decreased theta power for REM trials as compared to NOTREM trials. These effects were also found for the alpha and beta bands.

The present findings are in line with previous evidence of increases in theta power before stimulus presentation that were found to be related to more successful encoding of associative content 6 , 7 , 10 , 17 . Increases in theta power during encoding have been associated with better memory performance in experiments using SMEs before, especially when oscillatory data was gathered using non-invasive scalp EEG or MEG 10 . This line of evidence is supported by similar results posited by studies that did not use conventional SME contrasts but compared successful and unsuccessful memory formation based on secondary measures of episodic association, such as confidence ratings 5 , 40 . Interestingly, intracranial studies rarely reported exclusively positive relationships between theta power and memory performance, but instead rather both positive and negative associations 10 , 41 , 42 . In one study, however, Fell and colleagues found increases in hippocampal as well as rhinal cortex theta power in the pre-stimulus interval for later remembered words 19 . Taken together, the evidence seems to support the hypothesis of a positive association of theta power and memory performance in general, with the present results expanding the effect on audiovisual stimulus pairs and the encoding of their associations. This argument is supported by the positive relationship of SME magnitude and d ’ as a measure of memory performance found in this study. Similar results have been presented before in healthy adults 7 , supporting the assumption that fluctuations in pre-stimulus theta power might be considered behaviorally relevant. As the maximum effect in the present study was found in right lateral parietal regions, one could speculate about the effect’s origin in multisensory processing areas. The angular gyrus, for example, has been strongly associated with the processing of multimodal information, especially combining pieces of sensory information 43 . However, this assumption should be treated with caution due to the spatial limitations of EEG measurements.

In addition to the pre-stimulus difference in theta power, a similar effect was found in the post-stimulus time interval during encoding. Theta power decreased further in NOTREM trials than in REM trials between 500 ms and 900 ms after stimulus onset. This finding is in line with previous reports on higher theta power during encoding being associated with better memory performance 44 , 45 , 46 , 47 . Furthermore, visual source memory was found to be accompanied by increased post-stimulus theta power 17 . The present findings show that the encoding of associations of information from different modalities can be similarly modulated by theta activity. However, judging from the topographic distributions of both pre-stimulus and post-stimulus activity, one could assume that mechanisms by which fluctuations in theta power are involved might differ. While differences in pre-stimulus theta power are centered on right parietal and anterior frontal regions, post-stimulus effects peak at central and prefrontal locations. Despite the differing topographies, REM-NOTREM differences in pre- and post-stimlus theta power were found to positively correlate. Interestingly, there was no significant correlation in right parietal locations, but in right frontal areas instead. These results indicate the possibility of differential roles of pre- and post-stimulus theta activity for the formation of crossmodal associations, while still suggesting functional connectedness. Although one cannot conclude any form of causal effect from the present results, one could speculate that pre-stimulus oscillations might affect the memorization of audiovisual associations indirectly by modifying oscillations during the encoding. Further studies are needed to investigate a potentially causal relationship and the mechanisms that might be involved.

Apart from the effects observed in the theta band, our analyses revealed similar differences between REM and NOTREM trials for alpha and low beta activity, showing higher pre-stimulus power for REM trials in both frequency bands. Consequently, the present evidence suggests that associative memory performance may also benefit from increased alpha and beta activity before the encoding of the stimulus is required. This concept aligns with previous research that has explored the links between memory and alpha or beta activity, albeit in terms of preparatory mechanisms and attention. The involvement of alpha activity, for instance, has been thought of as inhibiting task-irrelevant processes to facilitate the encoding of items 9 , 23 , 48 , 49 , 50 . Specifically, positive alpha SMEs in the pre- and post-stimulus intervals as observed in the present work might indicate that already encoded information is being suppressed in favor of the upcoming and then current stimulus pair, respectively 30 , 51 , 52 . As participants were presented with a multitude of audiovisual pairs that required memorization in each experimental run, it seems plausible that the encoding of a pair would benefit from the suppression of other stimuli that were shown during the encoding phase. These considerations are supported by the positive correlation of REM-NOTREM differences in alpha power and memory performance in the present work, suggesting a role of alpha oscillations and subsequently top-down processes that might be at least as relevant as the proposed theta-based mechanism of binding information.

Similarly, beta activity has been suggested to indicate cognitive preparation processes, as increased oscillatory power was measured for intentional encoding as compared to incidental 11 . These preparation processes were also proposed to be independent of the modality of the stimuli, suggesting the involvement of attentional processes 6 . It seems reasonable to assume that the successful encoding of crossmodal associations recorded in the present study would benefit from preparatory attentional processes as well, as participants were also explicitly instructed to memorize these associations. In contrast, we observed a strong negative effect in the late post-stimulus interval primarily in the beta band. Decreases in the beta band have been theorized to reflect semantic processing of to-be-encoded items 30 , 47 , and are assumed to originate in the left prefrontal cortex 53 . Although one must account for spatial inaccuracies when interpreting EEG results, the present findings point in a similar direction, as the maximum effect for this SME was observed in left frontal areas. One could speculate that oscillations in the beta range serve multiple purposes depending on whether or not actual stimulus material needs to be processed or not. Positive SMEs before stimulus onset might reflect stimulus-independent preparatory processes, while negative SMEs during stimulus presentation could be interpreted as a marker of semantic processing. Furthermore, as the effect is mostly centered around left frontal areas instead of visual or auditory locations, one could argue that the semantic processing takes place independent of sensory modality, but on the level of associations.

The topographies of pre-stimulus SMEs further support the notion that theta as well as alpha and beta oscillations might contribute to successful memory formation in different ways. While power differences in the theta range were most pronounced in right parietal areas, the effects in the alpha range were centered on left temporal and frontal locations. Effects in the beta range were found to be strongest around left parietal and right frontal areas. Frontal midline theta oscillations have long since been associated with episodic memory formation and retrieval 54 , while sections of the parietal lobe are usually associated with multisensory association processes 55 , 56 . The different topographical distributions of power differences for the pre-stimulus interval could thus be interpreted as the involvement of different cognitive processes. Considering the results from pre- and post-stimulus activity, we suggest increased pre-stimulus theta power to represent non-general preparatory processes specifically for binding, while being a marker for the actual binding of information during encoding. One could assume that these binding-specific processes are modulated by processes of task-specific inhibition in the alpha, as well as preparatory and semantic processing in the beta range. However, as evidence for spatial patterns is limited in EEG, future studies will need to test the contribution of different brain areas to the reported effects by investigating differences in BOLD signal using fMRI measurements, as well as address the question of which frequency band might be the primary driver of subsequent memory effects in the encoding of crossmodal associations.

No differences were found between REM and NOTREM trials in terms of phase-based connectivity in the pre-stimulus and post-stimulus intervals. Thus, the evidence could not support our second hypothesis, suggesting that functional connectivity between visual and auditory areas might not be beneficial for the encoding of crossmodal associations. These results are not in line with several previous studies that were able to establish a phase-based relation between auditory and visual areas by oscillating audiovisual stimulus pairs in a theta frequency for differing degrees of synchrony 20 , 21 . Memory performance was found to be best when stimuli were not shown at a phase offset and oscillated at a frequency of 4 Hz. Under the assumption that the phase synchrony at stimulus onset was involved in the effect that the authors found, the present results might point in the same direction, although the effect is ostensibly weaker. In another study, pre-stimulus theta connectivity within the default mode network showed the lowest prediction accuracy when predicting associative memory performance as compared to other frequency bands 33 . Interestingly, the authors reported generally higher prediction accuracies based on connectivity measures calculated for the post-stimulus interval. Notably, other lines of evidence suggest that pre-stimulus theta phase may only be connected to successful, but not to the unsuccessful encoding of associative pairings, while not observing any significant effects for pre-stimulus theta power 34 . However, the present results could also be interpreted in a way such that cortical connectivity might only play an ancillary role in binding crossmodal information for long-term memory. Instead, it could be speculated that sensory areas are phase-locked to cells in the hippocampus individually but are not functionally connected to each other for the binding process. Indeed, hippocampal projections have been suggested to drive theta oscillations in neocortical areas 57 . Furthermore, theta oscillations have been reported to reflect the dynamic integration of information from multiple sources 58 , as well as present a mechanism to functionally align the hippocampus to prefrontal cortices during recollection 40 . Additional evidence from animal studies points towards the importance of hippocampal CA1 cells for the integration of not only spatial but different kinds of sensory information from stimuli whose presentation overlapped in the time domain 14 . The authors argued that the hippocampus organizes relational networks for episodic memory, integrating phase-locked information coming in from sensory modalities. However, further research into phase-based connectivity between the hippocampus and sensory areas in the neocortex is needed, as the present results cannot account for oscillatory activity in deeper layers of the brain.

Finally, we also investigated oscillatory activity before and during retrieval. The analysis revealed that power significantly decreased in the post-stimulus interval for trials in which already-shown stimuli were remembered. This effect was found not only in the theta band but also in alpha and beta oscillations. As most studies investigating oscillatory mechanisms in episodic memory focus on effects during or before encoding, evidence on desynchronization during retrieval in the theta band has been rarely presented before. Some studies presented evidence on a positive relationship between theta power during retrieval and successful episodic memory 5 , whereas work in the context of interference and interference resolution reported positive as well as negative effects 59 , 60 . In one study, Pastötter and Bäuml found decreases in power that were associated with better memory performance only in high theta frequencies, while lower frequencies showed increases instead 60 . The present results are only partially in line with the previous evidence, as decreases in theta power associated with better memory performance were also found in the lower frequency of the theta band. One possible explanation could be that the power in REM trials decreased further due to longer processing time of the auditory stimuli of the pairs 61 . This decrease could then be interpreted as a positive effect for behavior. On another note, decreases in alpha power during the retrieval of associative information have been shown before. When participants were required to remember associations between words, Martín-Buro and colleagues found post-stimulus decreases between 10 and 12 Hz as early as 0.5 s after stimulus onset, predominantly in left parietal areas 62 . By comparing different degrees of successful encoding the authors suggested that decreases in alpha power during retrieval might reflect the accumulation of mnemonic evidence. Although, in the present study, successful trials were compared to trials with unsuccessful encoding, one could argue that the results might reflect a similar gradient of mnemonic evidence accumulation even for associations between different modalities, given that the peak effects were also found in left parietal areas. However, interpreting these results should be done with caution, as no hypotheses were formulated regarding the effects of oscillatory activity before and during retrieval. We recommend further research focusing explicitly on oscillatory activity during retrieval to expand understanding in that matter.

This study investigated subsequent memory effects for oscillatory activity in the theta, alpha and low beta frequency range. Specifically, differences in oscillatory power and naturally occurring phase-based connectivity between later remembered and not remembered audiovisual stimulus pairs were analyzed. Importantly, theta power was found to differentiate between successful and unsuccessful encoding already prior to the stimulus presentation, i.e. in the pre-stimulus interval. The magnitude of this effect was found to be directly related to memory performance. Similar effects were observed in the alpha band and, to a lesser degree, in the beta band. In contrast, only weak evidence was observed for the assumed role of phase-based connectivity between visual and auditory brain areas for memory performance. The present findings reinforce the notion that theta band activity might be relevant in binding information from different modalities for episodic memory, and, more generally, highlight the impact of brain states before stimulus presentation on their subsequent processing. We argue that the theta-based binding mechanism might work in conjunction with inhibitory, as well as preparatory and semantic processes represented by alpha and beta oscillations, respectively, that benefit the encoding of crossmodal associations. Further research is needed to elucidate the interactions between oscillations of different frequencies, as well as the involvement of hippocampal theta oscillations in cortical processes for crossmodal associative memory.

Data availability

All data and code can be made available upon request through a data sharing agreement with the authors.

Salari, N., Büchel, C. & Rose, M. Functional dissociation of ongoing oscillatory brain states. PLoS ONE 7 (5), e38090. https://doi.org/10.1371/JOURNAL.PONE.0038090 (2012).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Roberts, D. M., Fedota, J. R., Buzzell, G. A., Parasuraman, R. & McDonald, C. G. Prestimulus oscillations in the alpha band of the EEG are modulated by the difficulty of feature discrimination and predict activation of a sensory discrimination process. J. Cogn. Neurosci. 26 (8), 1615–1628. https://doi.org/10.1162/JOCN_A_00569 (2014).

Article   PubMed   Google Scholar  

Van Dijk, H., Schoffelen, J. M., Oostenveld, R. & Jensen, O. Prestimulus oscillatory activity in the alpha band predicts visual discrimination ability. J. Neurosci. 28 (8), 1816–1823. https://doi.org/10.1523/JNEUROSCI.1853-07.2008 (2008).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bengson, J. J., Kelley, T. A., Zhang, X., Wang, J. L. & Mangun, G. R. Spontaneous neural fluctuations predict decisions to attend. J. Cogn. Neurosci. 26 (11), 2578–2584. https://doi.org/10.1162/JOCN_A_00650 (2014).

Addante, R. J., Watrous, A. J., Yonelinas, A. P., Ekstrom, A. D. & Ranganath, C. Prestimulus theta activity predicts correct source memory retrieval. Proc. Natl. Acad. Sci. U. S. A. 108 (26), 10702–10707. https://doi.org/10.1073/pnas.1014528108 (2011).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Scholz, S., Schneider, S. L. & Rose, M. Differential effects of ongoing EEG beta and theta power on memory formation. PLoS One 12 (2), e0171913. https://doi.org/10.1371/journal.pone.0171913 (2017).

Winterling, S. L., Shields, S. M. & Rose, M. Reduced memory-related ongoing oscillatory activity in healthy older adults. Neurobiol. Aging 79 , 1–10. https://doi.org/10.1016/j.neurobiolaging.2019.03.012 (2019).

Salari, N. & Rose, M. Dissociation of the functional relevance of different pre-stimulus oscillatory activity for memory formation. Neuroimage 125 , 1013–1021. https://doi.org/10.1016/j.neuroimage.2015.10.037 (2016).

Klimesch, W., Fellinger, R. & Freunberger, R. Alpha oscillations and early stages of visual encoding. Front. Psychol. https://doi.org/10.3389/fpsyg.2011.00118 (2011).

Article   PubMed   PubMed Central   Google Scholar  

Herweg, N. A., Solomon, E. A. & Kahana, M. J. Theta oscillations in human memory. Trends Cogn. Sci. 24 (3), 208–227. https://doi.org/10.1016/j.tics.2019.12.006 (2020).

Schneider, S. L. & Rose, M. Intention to encode boosts memory-related pre-stimulus EEG beta power. Neuroimage 125 , 978–987. https://doi.org/10.1016/j.neuroimage.2015.11.024 (2016).

Buzsáki, G. & Moser, E. I. Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nat. Neurosci. 16 (2), 130–138. https://doi.org/10.1038/nn.3304 (2013).

Huxter, J., Burgess, N. & O’Keefe, J. Independent rate and temporal coding in hippocampal pyramidal cells. Nature 425 (6960), 828–832. https://doi.org/10.1038/nature02058 (2003).

Terada, S., Sakurai, Y., Nakahara, H. & Fujisawa, S. Temporal and rate coding for discrete event sequences in the hippocampus. Neuron 94 (6), 1248-1262.e4. https://doi.org/10.1016/J.NEURON.2017.05.024 (2017).

Article   CAS   PubMed   Google Scholar  

Gruber, M. J., Watrous, A. J., Ekstrom, A. D., Ranganath, C. & Otten, L. J. Expected reward modulates encoding-related theta activity before an event. Neuroimage 64 (1), 68–74. https://doi.org/10.1016/j.neuroimage.2012.07.064 (2013).

Merkow, M. B., Burke, J. F., Stein, J. M. & Kahana, M. J. Prestimulus theta in the human hippocampus predicts subsequent recognition but not recall. Hippocampus 24 (12), 1562–1569. https://doi.org/10.1002/HIPO.22335 (2014).

Staudigl, T. & Hanslmayr, S. Theta oscillations at encoding mediate the context-dependent nature of human episodic memory. Curr. Biol. 23 (12), 1101–1106. https://doi.org/10.1016/j.cub.2013.04.074 (2013).

Haque, R. U., Wittig, J. H., Damera, S. R., Inati, S. K. & Zaghloul, K. A. Cortical low-frequency power and progressive phase synchrony precede successful memory encoding. J. Neurosci. 35 (40), 13577–13586. https://doi.org/10.1523/JNEUROSCI.0687-15.2015 (2015).

Fell, J. et al. Medial temporal theta/alpha power enhancement precedes successful memory encoding: Evidence based on intracranial EEG. J. Neurosci. 31 (14), 5392–5397. https://doi.org/10.1523/JNEUROSCI.3668-10.2011 (2011).

Clouter, A., Shapiro, K. L. & Hanslmayr, S. Theta phase synchronization is the glue that binds human associative memory. Curr. Biol. 27 (20), 3143-3148.e6. https://doi.org/10.1016/j.cub.2017.09.001 (2017).

Wang, D., Clouter, A., Chen, Q., Shapiro, K. L. & Hanslmayr, S. Single-trial phase entrainment of theta oscillations in sensory regions predicts human associative memory performance. J. Neurosci. 38 (28), 6299–6309. https://doi.org/10.1523/JNEUROSCI.0349-18.2018 (2018).

Klimesch, W. Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn. Sci. 16 (12), 606–617. https://doi.org/10.1016/j.tics.2012.10.007 (2012).

Strunk, J. & Duarte, A. Prestimulus and poststimulus oscillatory activity predicts successful episodic encoding for both young and older adults. Neurobiol. Aging 77 , 1–12. https://doi.org/10.1016/j.neurobiolaging.2019.01.005 (2019).

Waldhauser, G. T., Johansson, M. & Hanslmayr, S. Alpha/beta oscillations indicate inhibition of interfering visual memories. J. Neurosci. 32 (6), 1953–1961. https://doi.org/10.1523/JNEUROSCI.4201-11.2012 (2012).

Sokol, S. Visually evoked potentials: Theory, techniques and clinical applications. Surv. Ophthalmol. 21 (1), 18–44. https://doi.org/10.1016/0039-6257(76)90046-1 (1976).

Mulert, C. et al. Auditory cortex and anterior cingulate cortex sources of the early evoked gamma-band response: Relationship to task difficulty and mental effort. Neuropsychologia 45 (10), 2294–2306. https://doi.org/10.1016/j.neuropsychologia.2007.02.020 (2007).

Leicht, G. et al. Reduced early auditory evoked gamma-band response in patients with schizophrenia. Biol. Psychiatry 67 (3), 224–231. https://doi.org/10.1016/j.biopsych.2009.07.033 (2010).

Otten, L. J., Quayle, A. H., Akram, S., Ditewig, T. A. & Rugg, M. D. Brain activity before an event predicts later recollection. Nat. Neurosci. 9 (4), 489–491. https://doi.org/10.1038/nn1663 (2006).

Gruber, M. J. & Otten, L. J. Voluntary control over prestimulus activity related to encoding. J. Neurosci. 30 (29), 9793–9800. https://doi.org/10.1523/JNEUROSCI.0915-10.2010 (2010).

Hanslmayr, S., Staudigl, T. & Fellner, M. C. Oscillatory power decreases and long-term memory: The information via desynchronization hypothesis. Front. Hum. Neurosci. 6 , 1–20. https://doi.org/10.3389/fnhum.2012.00074 (2012).

Article   Google Scholar  

Park, H. & Rugg, M. D. Prestimulus hippocampal activity predicts later recollection. Hippocampus 20 (1), 24–28. https://doi.org/10.1002/hipo.20663 (2010).

Addante, R. J., de Chastelaine, M. & Rugg, M. D. Pre-stimulus neural activity predicts successful encoding of inter-item associations. Neuroimage 105 , 21–31. https://doi.org/10.1016/j.neuroimage.2014.10.046 (2015).

Kim, D., Jeong, W., Kim, J. S. & Chung, C. K. Single-trial EEG connectivity of default mode network before and during encoding predicts subsequent memory outcome. Front. Syst. Neurosci. https://doi.org/10.3389/fnsys.2020.591675 (2020).

Cruzat, J. et al. The phase of Theta oscillations modulates successful memory formation at encoding. Neuropsychologia 154 , 107775. https://doi.org/10.1016/j.neuropsychologia.2021.107775 (2021).

Staudigl, T., Vollmar, C., Noachtar, S. & Hanslmayr, S. Temporal-pattern similarity analysis reveals the beneficial and detrimental effects of context reinstatement on human memory. J. Neurosci. 35 (13), 5373–5384. https://doi.org/10.1523/JNEUROSCI.4198-14.2015 (2015).

Parise, C. V. & Spence, C. Audiovisual crossmodal correspondences and sound symbolism: A study using the implicit association test. Exp. Brain Res. 220 (3–4), 319–333. https://doi.org/10.1007/s00221-012-3140-6 (2012).

Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. https://doi.org/10.1155/2011/156869 (2011).

Vinck, M., Oostenveld, R., Van Wingerden, M., Battaglia, F. & Pennartz, C. M. A. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55 (4), 1548–1565. https://doi.org/10.1016/j.neuroimage.2011.01.055 (2011).

Morey, R. D. Confidence intervals from normalized data: A correction to Cousineau (2005). Tutor. Quant. Methods Psychol. 4 (2), 61–64. https://doi.org/10.20982/tqmp.04.2.p061 (2008).

Herweg, N. A. et al. Theta-alpha oscillations bind the hippocampus, prefrontal cortex, and striatum during recollection: Evidence from simultaneous EEG–fMRI. J. Neurosci. 36 (12), 3579–3587. https://doi.org/10.1523/JNEUROSCI.3629-15.2016 (2016).

Greenberg, J. A., Burke, J. F., Haque, R., Kahana, M. J. & Zaghloul, K. A. Decreases in theta and increases in high frequency activity underlie associative memory encoding. Neuroimage 114 , 257–263. https://doi.org/10.1016/J.NEUROIMAGE.2015.03.077 (2015).

Weidemann, C. T. et al. Neural activity reveals interactions between episodic and semantic memory systems during retrieval. J. Exp. Psychol. Gen. 148 (1), 1–12. https://doi.org/10.1037/XGE0000480 (2019).

Seghier, M. L. The angular gyrus: Multiple functions and multiple subdivisions. Neuroscience 19 (1), 43–61. https://doi.org/10.1177/1073858412440596 (2013).

Khader, P. H., Jost, K., Ranganath, C. & Rösler, F. Theta and alpha oscillations during working-memory maintenance predict successful long-term memory encoding. Neurosci. Lett. 468 (3), 339–343. https://doi.org/10.1016/J.NEULET.2009.11.028 (2010).

Hanslmayr, S. et al. The relationship between brain oscillations and BOLD signal during memory formation: A combined EEG-fMRI study. J. Neurosci. 31 (44), 15674–15680. https://doi.org/10.1523/JNEUROSCI.3140-11.2011 (2011).

Osipova, D. et al. Theta and gamma oscillations predict encoding and retrieval of declarative memory. J. Neurosci. 26 (28), 7523–7531. https://doi.org/10.1523/JNEUROSCI.1948-06.2006 (2006).

Hanslmayr, S., Spitzer, B. & Bäuml, K. H. Brain oscillations dissociate between semantic and nonsemantic encoding of episodic memories. Cereb. Cortex 19 (7), 1631–1640. https://doi.org/10.1093/cercor/bhn197 (2009).

Bonnefond, M. & Jensen, O. The role of gamma and alpha oscillations for blocking out distraction. Commun. Integr. Biol. 6 (1), e22702. https://doi.org/10.4161/cib.22702 (2013).

Minarik, T., Berger, B. & Sauseng, P. The involvement of alpha oscillations in voluntary attention directed towards encoding episodic memories. Neuroimage 166 , 307–316. https://doi.org/10.1016/j.neuroimage.2017.10.064 (2018).

Payne, L., Guillory, S. & Sekuler, R. Attention-modulated alpha-band oscillations protect against intrusion of irrelevant information. J. Cogn. Neurosci. 25 (9), 1463–1476. https://doi.org/10.1162/jocn_a_00395 (2013).

Jensen, O., Gelfand, J., Kounios, J. & Lisman, J. E. Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short-term memory task. Cereb. Cortex 12 (8), 877–882. https://doi.org/10.1093/cercor/12.8.877 (2002).

Meeuwissen, E. B., Takashima, A., Fernández, G. & Jensen, O. Increase in posterior alpha activity during rehearsal predicts successful long-term memory formation of word sequences. Hum. Brain Mapp. 32 (12), 2045–2053. https://doi.org/10.1002/hbm.21167 (2011).

Meeuwissen, E. B., Takashima, A., Fernández, G. & Jensen, O. Evidence for human fronto-central gamma activity during long-term memory encoding of word sequences. PLoS One 6 (6), e21356. https://doi.org/10.1371/journal.pone.0021356 (2011).

Hsieh, L. T. & Ranganath, C. Frontal midline theta oscillations during working memory maintenance and episodic encoding and retrieval. Neuroimage 85 , 721–729. https://doi.org/10.1016/j.neuroimage.2013.08.003 (2014).

Sereno, M. I. & Huang, R. S. Multisensory maps in parietal cortex. Curr. Opin. Neurobiol. 24 (1), 39–46. https://doi.org/10.1016/j.conb.2013.08.014 (2014).

Rohe, T. & Noppeney, U. Distinct computational principles govern multisensory integration in primary sensory and association cortices. Curr. Biol. 26 (4), 509–514. https://doi.org/10.1016/j.cub.2015.12.056 (2016).

Ekstrom, A. D. et al. Human hippocampal theta activity during virtual navigation. Hippocampus 15 (7), 881–889. https://doi.org/10.1002/HIPO.20109 (2005).

Feng, T., Silva, D., Foster, D. J. & Snyder, S. H. Dissociation between the experience-dependent development of hippocampal theta sequences and single-trial phase precession. J. Neurosci. 35 (12), 4890–4902. https://doi.org/10.1523/JNEUROSCI.2614-14.2015 (2015).

Khader, P. H. & Rösler, F. EEG power changes reflect distinct mechanisms during long-term memory retrieval. Psychophysiology 48 (3), 362–369. https://doi.org/10.1111/j.1469-8986.2010.01063.x (2011).

Pastötter, B. & Bäuml, K.-H.T. Distinct slow and fast cortical theta dynamics in episodic memory retrieval. Neuroimage 94 , 155–161. https://doi.org/10.1016/j.neuroimage.2014.03.002 (2014).

Robinson, C. W., Chadwick, K. R., Parker, J. L. & Sinnett, S. Listen to your heart: Examining modality dominance using cross-modal oddball tasks. Front. Psychol. https://doi.org/10.3389/FPSYG.2020.01643 (2020).

Martín-Buro, M. C., Wimber, M., Henson, R. N. & Staresina, B. P. Alpha rhythms reveal when and where item and associative memories are retrieved. J. Neurosci. 40 (12), 2510–2518. https://doi.org/10.1523/JNEUROSCI.1982-19.2020 (2020).

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Acknowledgements

The present research was supported by the German Research Foundation (DFG; SFB/Transregio 169, Project B3, and DFG RO 2653/9-1). We thank Marike Maack for the lively discussion and helpful comments on the manuscript, as well as Carla Mourkojannis and Donna Löding for their help in data acquisition.

Open Access funding enabled and organized by Projekt DEAL.

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Jan Ostrowski & Michael Rose

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Jan Ostrowski: Software, Validation, Formal Analysis, Investigation, Data Curation, Writing—Original Draft, Writing—Review and Editing, Visualization. Michael Rose: Conceptualization, Methodology, Software, Validation, Resources, Writing—Review and Editing, Supervision, Project administration, Funding acquisition.

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Ostrowski, J., Rose, M. Increases in pre-stimulus theta and alpha oscillations precede successful encoding of crossmodal associations. Sci Rep 14 , 7895 (2024). https://doi.org/10.1038/s41598-024-58227-z

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Received : 01 December 2023

Accepted : 26 March 2024

Published : 03 April 2024

DOI : https://doi.org/10.1038/s41598-024-58227-z

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