The over-reliance on null hypothesis significance testing and its accompanying tools has recently been challenged. An example of such a tool is statistical power analysis, which is used to determine how many participants are required to detect a minimally meaningful effect in the population at given levels of power and Type I error rate. To investigate how power analysis is currently used, we review the reporting of 443 power analyses in high-impact psychology journals in 2016 and 2017. We found that many pieces of information required for power analyses are not reported, and selected effect sizes are often chosen based on an inappropriate rationale. Accordingly, we argue that power analysis forces researchers to compromise in the selection of the different pieces of information. We offer that researchers should focus on tools beyond traditional power analysis when sample planning, such as precision-based power analysis or collecting the largest sample size possible
