Bayesian Methods Might Solve the Problems with Magnitude-based Inference

Abstract

The recent and interesting article by Sainani (1) addresses issues with using magnitude-based inference (MBI) as a stand-alone statistical method, namely, type I and type II error rates. Claims of MBI boasting superior error rates compared with standard null hypothesis significance testing are appealing and of interest to many sports science researchers, who often observe small treatment effects using small sample sizes. Sainani (1), among others (2,3), has highlighted concerns with MBI, with Sainani (1) recently providing empirical and mathematical evidence against the claims of superior error rates of MBI. Attempts to adopt more robust statistical methods for the challenges encountered in sports science should be commended. Bayesian estimation has been shown to overcome the concerns raised by Sainani (1) while providing more intuitive and practically interpretable results compared with the MBI approach