The aim of this paper is to close the knowledge-to-practice gap around statistical power. We demonstrate how four factors affect power: p value, effect size, sample size, and variance. This article further delves into the advantages and disadvantages of a priori versus post hoc power analyses, though we believe only understanding of the former is essential to addressing the present-day issue of reproducibility in research. Upon reading this paper, physician-scientists should have expanded their arsenal of statistical tools and have the necessary context to understand statistical fragility.

Mean, median, and mode are among the most basic and consistently used measures of central tendency in statistical analysis and are crucial for simplifying data sets to a single value. However, there is a lack of understanding of when to use each metric and how various factors can impact these values. The aim of this article is to clarify some of the confusion related to each measure and explain how to select the appropriate metric for a given data set. The authors present this work as an educational resource, ensuring that these common statistical concepts are better understood throughout the Orthopedic research community.

No abstract