Addressing Type S vs. Type M errors with Bayesian Hierarchical Modeling
In statistics, most statistical tests aim to trade off type I and type II errors. Type I error is the incorrect rejection of a true null hypothesis, in other words a false positive. Type II errors are the incorrect retaining a false null hypothesis; in other words, a false negative. Oftentimes, the null hypothesis is posed…