Casting New Light on Statistical Power: An Illuminating Analogy and Strategies to Avoid Underpowered Trials

Abstract

Abstract Current standards for methodological rigor and trial reporting underscore the critical issue of statistical power. Still, the chance of detecting most effects reported in randomized controlled trials in medicine and other disciplines is currently lower than winning a toss of a fair coin. Here we propose that investigators who retain a practical understanding of how statistical power works can proactively avoid the potentially devastating consequences of underpowered trials. We first offer a vivid, carefully constructed analogy that illuminates the underlying relationships among 3 of the 5 essential parameters—namely, statistical power, effect size, and sample size—while holding the remaining 2 parameters constant (type of statistical test and significance level). Second, we extend the analogy to a set of critical scenarios in which investigators commonly miss detecting intervention effects due to insufficient statistical power. Third, we highlight effective pragmatic strategies for the design and conduct of sufficiently powered trials, without increasing sample size.