An Average Power Primer: Clarifying Misconceptions about Average Power and Replicability
Cohen (1988) introduced power analysis for the planning of studies to reduce false negative (type-II error) rates in psychological science. After the replication crisis, the importance of a priori power analyses has gained increasing attention. However, the estimation of actual power of studies remains neglected. This article clarifies important differences between power analyses with hypothetical effect sizes to plan studies and power analyses of actual studies that have been completed. Knowing the actual power of completed studies is important because it can be used to assess publication bias. Sets of studies that have high success rates, but low power do not provide credible evidence for a hypothesis.