Reproducibility vs Replicability | Difference & Examples

The terms reproducibility, repeatability, and replicability are sometimes used interchangeably, but they mean different things.

  • A research study is reproducible when the existing data is reanalysed using the same research methods and yields the same results. This shows that the analysis was conducted fairly and correctly.
  • A research study is replicable (or repeatable) when the entire research process is conducted again, using the same methods but new data, and still yields the same results. This shows that the results of the original study are reliable.
Note
A study may be reproducible but not replicable.

A survey of 60 children between the ages of 12 and 16 shows that football and hockey are the most popular sports. Football received 20 votes and hockey 18.

An independent researcher reanalyses the survey data and also finds that 20 children chose football and 18 children chose hockey. This makes the research reproducible.

The researcher then decides to conduct the study all over again. Another 60 children between the ages of 12 and 16 take part in the study. This time the results show that tennis is the most popular sport, chosen 25 times. The research is therefore not replicable.

Why reproducibility and replicability matter in research

Reproducibility and replicability enhance the reliability of results. This allows researchers to check the quality of their own work or that of others, which in turn increases the chance that the results are valid and not suffering from research bias.

On the other hand, reproduction alone does not show whether the results are correct. As it does not involve collecting new data, reproducibility is a minimum necessary condition showing that findings are transparent and informative.

In order to make research reproducible, it is important to provide all the necessary raw data. This makes it so that anyone can run the analysis again, ideally recreating the same results. Omitted variables, missing data, or mistakes leading to information bias can lead to your research not being reproducible.

Example: Reproducible research
For your final research paper, you have interviewed consumers about their perceptions of food waste at the household level.

To make your research reproducible, you describe step by step how you collected and analysed your data. You also include all the raw data in the appendix: a list of the interview questions, the interview transcripts, and the coding sheet you used to analyse your interviews.

In this way, your readers can follow your process, seeing how you arrived at your research findings.

Sometimes researchers also conduct replication studies. These studies investigate whether researchers can arrive at the same scientific findings as an existing study while collecting new data and completing new analyses. 

Example: Replicable (or repeatable) research
You have come across an already-published research paper on consumer perceptions and knowledge about food waste at the household level in a rural state in the US.

The researchers administered an online survey, and the majority of the participants (58%) reported that they waste 10% or less of procured food. They also found that guilt and setting a good example were the main motivators for reducing food waste, rather than economic or environmental factors.

Together with your research group, you decide to conduct a replication study: you collect new data in the same state, this time via focus groups. Your findings are consistent with the initial study, which makes it replicable.

In other words, replication studies are conducted by the same researchers in order to verify the original result, or by other researchers using the same or similar methods.

Overall, repeatability and reproducibility ensure that scientists remain honest and do not invent or distort results to get better outcomes. In particular, testing for reproducibility can also be a way to catch any mistakes, biases, or inconsistencies in your data.

What is the replication crisis?

Unfortunately, findings from many scientific fields such as psychology, medicine, or economics often prove impossible to replicate. When other research teams try to repeat a study, they get a different result, suggesting that the initial study’s findings are not reliable.

Some factors contributing to this phenomenon include:

  • Unclear definition of key terms
  • Poor description of research methods
  • Lack of transparency in the discussion section
  • Unclear presentation of raw data
  • Poor description of data analysis undertaken

Publication bias can also play a role. Scientific journals are more likely to accept original (non-replicated) studies that report positive, statistically significant results that support the hypothesis.

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How to ensure reproducibility and replicability in your research

To make your research reproducible and replicable, it is crucial to describe, step by step, how to conduct the research. You can do so by focusing on writing a clear and transparent methodology section, using precise language and avoiding vague writing.

Transparent methodology section

In your methodology section, you explain in detail what steps you have taken to answer the research question. As a rule of thumb, someone who has nothing to do with your research should be able to repeat what you did based solely on your explanation.

For example, you can describe:

  • What type of research (quantitative, qualitative, mixed methods) you conducted
  • Which research method you used (interviews, surveys, etc.)
  • Who your participants or respondents are (e.g., their age or education level)
  • What materials you used (audio clips, video recording, etc.)
  • What procedure you used
  • What data analysis method you chose (such as the type of statistical analysis)
  • How you ensured reliability and validity
  • Why you drew certain conclusions, and on the basis of which results
  • In which appendix the reader can find any survey questions, interviews, or transcripts

Sometimes, parts of the research may turn out differently than you expected, or you may accidentally make mistakes. This is all part of the process! It’s important to mention these problems and limitations so that they can be prevented next time. You can do this in the discussion or conclusion, depending on the requirements of your study program.

Use of clear and unambiguous language

You can also increase the reproducibility and replicability/repeatability of your research by always using crystal-clear writing. Avoid using vague language, and ensure that your text can only be understood in one way. Careful description shows that you have thought in depth about the method you chose and that you have confidence in the research and its results.

Here are a few examples.

  • The participants of this study were children from a school.
  • The 67 participants of this study were elementary school children between the ages of 6 and 10.
  • The interviews were transcribed and then coded.
  • The semi-structured interviews were first summarised, transcribed, and then open-coded.
  • The results were compared with a t test.
  • The results were compared with an unpaired t test.

Frequently asked questions about reproducibility, replicability and repeatability

What’s the difference between reproducibility and replicability?

Reproducibility and replicability are related terms.

  • Reproducing research entails reanalyzing the existing data in the same manner.
  • Replicating (or repeating) the research entails reconducting the entire analysis, including the collection of new data
Why are reproducibility and replicability important?

Reproducibility and replicability are related terms.

  • A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
  • A successful replication shows that the reliability of the results is high.
How can you ensure reproducibility and replicability?

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

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Kassiani Nikolopoulou

Kassiani has an academic background in Communication, Bioeconomy and Circular Economy. As a former journalist she enjoys turning complex scientific information into easily accessible articles to help students.