Open data, preregistration and clear methods help others verify findings.
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.
Open data, preregistration and clear methods help others verify findings.
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 Null Hypothesis, Control Group, Experimental Design.
The Reproducibility Project in psychology replicated fewer than half the findings.
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.
The closest topics to Reproducibility in Science are Null Hypothesis, Control Group, Experimental Design, Statistical Power. Comparing them side by side is a good way to see how the ideas connect.
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.
Reproducibility in Science: Open data, preregistration and clear methods help others verify findings. Null Hypothesis: The default assumption of no effect or difference, which data may lead us to reject. Read both side by side and note what each one explains.
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 Null Hypothesis and explain the difference aloud. Finally, take the Lab Methods & Biostatistics quiz and come back to this page for any question you miss.
The default assumption of no effect or difference, which data may lead us to reject.
📊 Lab Methods & BiostatisticsA group that does not receive the treatment, used as a baseline for comparison.
📊 Lab Methods & BiostatisticsRandomisation, replication, blocking and blinding reduce bias and confounding.
📊 Lab Methods & BiostatisticsThe probability of detecting a true effect, determined by sample size, effect size and variability.
📊 Lab Methods & Biostatistics