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Researchers in psychology are frequently confronted with the issue of analysing multiple relationships simultaneously. For example, this could involve multiple outcome variables or multiple predictors in a regression framework. Current recommendations typically steer researchers toward familywise or false-discovery rate Type I error control to limit the probability of incorrectly rejecting the null hypothesis. Stepwise modified-Bonferroni procedures are suggested for following this recommendation. However, longstanding arguments against multiplicity control combined with a modern distaste for null hypothesis significance testing have warranted revisiting this debate. This paper is an exploration of both sides of the multiplicity control debate, with the goal of educating concerned parties regarding best practices for conducting multiple related tests.