Summary
Open data, preregistration and clear methods help others verify findings.
In depth
The replication crisis revealed that many results fail to replicate. Remedies: preregistration, registered reports, open code and data, and larger samples.
Share data, code and protocols. Use version control. Report negative results.
Remember: reproducibility means the same analysis gives the same result; replication means new data give similar results.
Reproducibility means that other researchers can obtain the same results using the same data and methods; replicability means finding consistent results in new experiments. Surveys such as the 2015 psychology project and cancer biology replication efforts found that many published results could not be reproduced.
Causes include small samples, flexible analyses and p-hacking, publication bias towards positive results, poor reporting of methods, unvalidated reagents and cell line misidentification. Remedies are preregistration of hypotheses and analysis plans, registered reports, open data and code, version control, detailed protocols, sharing materials and incentives for replication.
Example: ARRIVE guidelines improve reporting of animal studies.
Frequently asked questions
What is Reproducibility in Science?
Open data, preregistration and clear methods help others verify findings.
Which area of biology covers Reproducibility in Science, and how advanced is it?
Reproducibility in Science is one of the 51 topics in Lab Methods & Biostatistics. It is rated Advanced, so it suits learners who want specialist depth. Related topics in this area include Statistical Power, Meta-Analysis, Laboratory Automation.
Why does Reproducibility in Science matter?
Science is self-correcting only if findings are checked.
Can you give an example of Reproducibility in Science?
The Reproducibility Project in psychology replicated fewer than half the findings.
What should I learn before Reproducibility in Science?
Work through the Beginner topics in Lab Methods & Biostatistics first, such as Scientific Method, Hypothesis, Variables, then return to Reproducibility in Science. Advanced topics assume the Intermediate vocabulary.
What topics are related to Reproducibility in Science?
The closest topics to Reproducibility in Science are Statistical Power, Meta-Analysis, Laboratory Automation, P-Values and Multiple Testing. Comparing them side by side is a good way to see how the ideas connect.
How can I check that I understand Reproducibility in Science?
Read the summary and the in-depth section, then use the quick check on the interactive lesson for Reproducibility in Science. To practise the whole of Lab Methods & Biostatistics, use the Lab Methods & Biostatistics quiz.
How is Reproducibility in Science different from Statistical Power?
Reproducibility in Science: Open data, preregistration and clear methods help others verify findings. Statistical Power: The probability of detecting a true effect, determined by sample size, effect size and variability. Read both side by side and note what each one explains.
How should I study Reproducibility in Science step by step?
First, read the summary and write the definition in your own words. Second, read the in-depth section and note any example. Third, compare it with Statistical Power and explain the difference aloud. Finally, take the Lab Methods & Biostatistics quiz and come back to this page for any question you miss.