The Benefits of Reporting Critical-Effect-Size Values

Critical-effect-size values represent the smallest detectable effect that can reach statistical significance given a specific sample size, alpha level, and test statistic. It can be useful to calculate the critical effect size when designing a study and evaluate whether such effects are plausible. Reporting critical-effect-size values may be useful when the sample size has not been planned a priori, there is uncertainty about the expected sample size that can be collected, or researchers plan to analyze the data with a statistical hypothesis test. To assist researchers in calculating critical-effect-size values, we developed an R package that allows researchers to report critical-effect-size values for group comparisons, correlations, linear regressions, and meta-analyses. Reflecting on critical-effect-size values could benefit researchers during the planning phase of the study by helping them to understand the limitations of their research design. Critical-effect-size values are also useful when evaluating studies performed by other researchers when a priori power analyses were not performed, especially when nonsignificant results are observed.