This is because the statistical power of the test dramatically affects our capacity to interpret the P value and thus the test result. Many scientists who are not statisticians do not realize that the power of a test is equally relevant when considering statistically significant results, that is, when the null hypothesis appears to be untenable.
This omission reflects a general lack of awareness about this crucial issue, and we address this matter here.Īlthough statistical power is a central element in reliability 18, it is often considered only when a test fails to demonstrate a real effect (such as a difference between groups): a 'false negative' result (see Box 2 for a glossary of statistical terms used in this article). Yet a fundamental problem inherent in standard statistical methods, one that is pervasively linked to the lack of reproducibility in research, remains to be considered: the wide sample-to-sample variability in the P value. These discussions and initiatives all focused on a number of issues, including aspects of statistical reporting 9, levels of statistical power (i.e., sufficient statistical capacity to find an effect a 'statistically significant' finding) 10 and inclusion-exclusion criteria. In a recent meeting at the American Association for the Advancement of Science headquarters involving many of the major journals reporting biomedical science research, a common set of principles and guidelines was agreed upon for promoting transparency and reproducibility 8. There has been recent recognition, however, that the results of published research can be difficult to replicate 1, 2, 3, 4, 5, 6, 7, an awareness epitomized by a series in Nature entitled “Challenges in irreproducible research” and by the Reproducibility Initiative, a project intended to identify and reward reproducible research ( ).
Reproducible research findings are a cornerstone of the scientific method, providing essential validation.
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