Glossary // Glossary term Technical
Statistical power and sample size
Power is the probability that a study will detect an effect of a given size if that effect exists; it rises with sample size, effect size and measurement precision. Underpowered studies miss real effects, and, less obviously, the significant results they do report tend to exaggerate effect sizes and are less likely to reflect true effects. Power should be planned before data collection, typically for the smallest effect that would matter. How one could reason about recording a catharsis-like change of state: signals, features, inference and context, baselines, self-report, error costs and ethics, and why every step stays uncertain. Nothing on this site measures anyone. Every public claim on this site with its type, evidence level, confidence, sources and review date, and an appraisal of each reference. How claims are graded, how art is kept apart from evidence, and how to report a problem. The works the definition rests on, with the level of evidence each was appraised at. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. doi:10.1038/nrn3475 Cited for the claims that low power reduces the chance of detecting true effects, lowers the probability that significant results reflect true effects and inflates estimated effect sizes. Figures from the analysis were not re-verified from an abstract and are not stated on the site.Where the site uses it
Studies behind it