The Scientific Method: Steps, Examples and Real Data

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Quick answerThe scientific method is a systematic way of answering questions with evidence: you observe something, ask a question, form a testable hypothesis, test it with an experiment or data, analyse the results, and draw a conclusion that others can check and repeat. It is important because it reduces the influence of personal opinion and makes results reproducible.

Below we explain each step in plain language, then run the whole method on a real question using ScholarsColab’s own research data, so you can see every step in action.

The steps of the scientific method

Step What you do Example
1. Observation Notice a pattern or a problem Some papers are cited far more than others
2. Question Turn it into a clear research question Are review articles cited more than original research?
3. Hypothesis Propose a testable answer, and its opposite (null hypothesis) Review articles receive more citations; null: no difference
4. Prediction Say what you should see if the hypothesis is true Median citations of reviews will be higher
5. Test Collect data or run an experiment designed to check the prediction Compare citations of reviews and original papers
6. Analysis Use appropriate statistics to judge the result Compare medians; test whether the gap could be chance
7. Conclusion Accept, reject or refine the hypothesis, and state limits Reviews are cited more in this dataset
8. Replication Others repeat the work to confirm it Repeat with another set of researchers
1. Observenotice a pattern?2. Ask a questionmake it precise3. Hypothesisa testable answer4. Testexperiment or data5. Analysestatistics, chance6. Concludeaccept, reject, limitsReplicateothers repeat it
Figure 1. The steps of the scientific method as a cycle, with replication by other researchers at the centre.

Example: the scientific method on real data

Here is the whole method applied to one question, using the data behind our research methodology guides. Our data: 42,750 papers, mostly published between 1998 and 2026, by 1,014 ScholarsColab mentors (data updated 1 November 2026).

Step 1–2
Observation and question. Looking at mentors’ publication lists, review articles often seem to sit near the top by citations. Question: are review articles cited more than original research?

Step 3–4
Hypothesis and prediction. Hypothesis: review articles receive more citations than original research papers. Null hypothesis: there is no difference. Prediction: if the hypothesis is right, the typical (median) review will have more citations.

Step 5
Test. Papers are grouped by type, identified from their titles:

Median citations by type of paperOriginal research621,308 papersReview articles12993 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.

Step 6
Analysis. Among 22,437 papers published between 2010 and 2022, review articles received a median of 12 citations, compared with 6 for original research papers: about 2× as many.

Step 7
Conclusion, with its limits. In this dataset, the evidence supports the hypothesis. But a good conclusion also states what the test cannot show: older papers have had more time to be cited (Papers from 2010–2014 have a median of 8 citations, while papers from 2025–2026 have 0. Newer papers have simply had less time to be cited.), the data covers one group of researchers, and paper types are identified from titles only. These are the study’s limitations.

Step 8
Replication. Because the data updates daily as mentors and publications are added, anyone can check the same comparison again later. You can also download the data below and repeat the analysis yourself.

Why the null hypothesis matters. The scientific method does not try to prove a hypothesis right; it tries to show that the “no difference” explanation is unlikely. That is why a result is reported with a test of chance, such as a p-value or a confidence interval.

Characteristics of a scientific study

  • Testable: the hypothesis can be shown to be wrong by evidence.
  • Systematic: data is collected and analysed with a plan decided in advance.
  • Objective: results do not depend on who runs the study.
  • Reproducible: others can repeat the method and get similar results.
  • Transparent about limits: uncertainties and biases are reported, not hidden.

Frequently asked questions

What are the steps of the scientific method?

Observation, research question, hypothesis, prediction, test (experiment or data collection), analysis, conclusion, and replication by others.

Why is the scientific method important?

It replaces opinion with evidence that others can check. Because every step is planned and reported, results can be repeated, challenged and improved.

Is the scientific method only for experiments?

No. It applies to observational studies, surveys, simulations and data analysis too. What matters is a testable hypothesis, a planned way to test it, and honest reporting of the results and their limits.

What is the difference between a hypothesis and a theory?

A hypothesis is a specific, testable prediction for one study. A theory is a broad explanation supported by many tested hypotheses over time.

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 1 November 2026, from https://scholarscolab.com/research-methodology/. Licensed under CC BY 4.0.Download the data (CSV) →

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Mentors whose published work uses this approach:

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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.