Where is the Power in a Power Analysis?

Strategic planning of research involves predicting the number of replications of the experiment needed to detect an expected effect. The power analysis to determine sample size for the proposed experiment requires known or estimated characteristics of existing distributions. When used well, power analysis reduces the risk of statistical errors, wasted efforts, and temptations to twist ensuing analyses to eke out a 'significant' result after data collections conclude. This editorial highlights some examples of how this process goes awry. Sections are dedicated to the role of researchers, the case for 'pilot' studies, and the critical involvement of reviewers as arbiters of best practices. Throughout, the importance of reporting standard statistical data to support conclusions is identified as the platform for enabling future power analyses. Logical, evidence-based pre-planning of studies and implementing standard statistical reporting increases transparency of research, the likelihood of a study to be cited in the future, and enhances the body of research that exercise scientists collectively build.