Research Hypothesis: How to Formulate and Test One, with Real Data

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Quick answerA research hypothesis is a clear, testable prediction about the relationship between variables, made before you look at the results. It is usually stated as an alternative hypothesis (there is an effect or difference) and tested against a null hypothesis (there is no effect or difference).

Below, we explain hypotheses in plain language and then formulate and test two real hypotheses on live data: the publications of PhD researchers on ScholarsColab.

Hypothesis essentials at a glance

Term Meaning Example
Research question What you want to find out Do review articles receive more citations?
Alternative hypothesis (H1) Your prediction that an effect or difference exists Review articles receive more citations than original research papers.
Null hypothesis (H0) The default assumption of no effect or difference Review articles and original papers receive the same number of citations.
Directional hypothesis Predicts the direction of the effect “More citations”, not just “different”
Non-directional hypothesis Predicts a difference, without a direction “A different number of citations”

What makes a good hypothesis

  • Testable: it can be checked with data you can actually collect.
  • Specific: it names the variables and the expected relationship.
  • Grounded: it follows from theory or earlier findings, not a guess.
  • Falsifiable: some possible result would show it to be wrong.
  • Stated in advance: before you analyse the data, so the result cannot shape the prediction.

Example: testing a hypothesis on ScholarsColab’s data

To explain hypotheses, we formulate one and test it on ScholarsColab’s own data: 41,792 papers, mostly published between 1998 and 2026, by 967 ScholarsColab mentors (data updated 15 October 2026).

Step 1
Start with a question. Do review articles (papers that summarise many earlier studies) get more citations than original research papers?

Step 2
State your prediction: the alternative hypothesis (H1). “Review articles receive more citations than original research papers.”

Step 3
State the opposite: the null hypothesis (H0). “Review articles and original research papers receive the same number of citations.” The test asks whether the data give enough reason to reject this.

H1: reviews get more citationsyour prediction
vs
H0: no differencethe default

Step 4
Plan a fair test before looking. Older papers have had more time to be cited, so we decided in advance to compare only papers published in the same years.

Step 5
Look at the result.

Median citations by type of paperOriginal research620,930 papersReview articles13980 papers
Median citations per paper, by type of paper. Papers published 2010–2022 by ScholarsColab mentors; type identified from the title. Source: ScholarsColab Research Methods Index.
Among 22,045 papers published between 2010 and 2022, review articles received a median of 13 citations, compared with 6 for original research papers: about 2.2× as many.

Step 6
Decide. If reviews clearly receive more citations, the data support H1, so we reject H0 for this group of researchers. A statistical test would add a p-value to show how confident we can be. If the numbers ever changed and the difference disappeared, this page would say so automatically: a hypothesis can always turn out to be wrong.

Supported is not proven. The result holds for the papers we tested. Our mentors are not a random sample of all researchers, so it may not hold everywhere.

How to formulate a hypothesis for your own study

  1. Start from your research question and the variables it involves.
  2. Read the existing literature to see what earlier findings suggest.
  3. State the expected relationship between the independent and dependent variables, with a direction if theory supports one.
  4. State the matching null hypothesis.
  5. Decide how you will test it: the data, the measure of each variable, and the statistical test.
  6. Fix it before analysis, ideally by recording it in your proposal or a study registration.
Field Example hypothesis (H1)
Medicine Patients receiving drug A have lower blood pressure after eight weeks than patients receiving a placebo.
Engineering Adding 2% nano-silica increases the compressive strength of concrete.
Management Employee engagement is positively related to job performance.
Computer science A larger training dataset increases classification accuracy.
Agriculture Biofertiliser application increases wheat yield compared with no application.

Common mistakes reviewers point out

  • Hypotheses that cannot be tested, because the variables are vague or cannot be measured.
  • Changing the hypothesis after seeing the results, presenting an exploratory finding as if it had been predicted.
  • Saying a hypothesis was “proven” rather than supported.
  • Treating a non-significant result as proof of no effect. Failing to reject H0 is not the same as showing H0 is true.

Frequently asked questions

What is a hypothesis in research?

A hypothesis is a clear, testable prediction about the relationship between variables, stated before the data are analysed.

What is the difference between a null and an alternative hypothesis?

The null hypothesis states that there is no effect or difference. The alternative hypothesis states that there is one. Statistical tests assess whether the data give enough evidence to reject the null hypothesis.

What is the difference between a hypothesis and a research question?

A research question asks what you want to find out. A hypothesis gives your predicted answer to that question in a form that can be tested.

Does every study need a hypothesis?

No. Studies that test relationships usually state hypotheses, while exploratory, descriptive and many qualitative studies are guided by research questions instead.

What happens if my hypothesis is not supported?

That is a valid result. Report it honestly, discuss possible reasons, and consider what it means for the theory. Unsupported hypotheses are part of how knowledge advances.

Use this data in your own work

Use this data in your own research. Download the aggregated figures behind these charts (counts, medians and shares by type of paper, period, method and discipline) as a CSV file. Please cite it as:ScholarsColab (2026). ScholarsColab Research Methods Index [Data set]. Vidhilekha Soft Solutions Pvt Ltd. Retrieved 15 October 2026, from https://scholarscolab.com/research-methodology/. Licensed under CC BY 4.0.Download the data (CSV) →

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About the data on this page. The ScholarsColab Research Methods Index is built from the Google Scholar publication lists of PhD researchers who mentor on ScholarsColab, and is updated daily as mentors and publications are added. Methods, designs and paper types are identified from titles, so papers whose titles do not mention them are not counted in those groups. The mentors are not a random sample of all researchers (engineering and computer science are strongly represented), so the figures describe this group of researchers and may not apply to research as a whole.