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.

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.

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.

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. 

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 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.

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 reanalysing 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.