Type I error rates are not usually inflated

The inflation of Type I error rates is thought to be one of the causes of the replication crisis. Questionable research practices such as p-hacking are thought to inflate Type I error rates above their nominal level, leading to unexpectedly high levels of false positives in the literature and, consequently, unexpectedly low replication rates. In this article, I offer an alternative view. I argue that questionable and other research practices do not inflate Type I error rates that are relevant to the statistical inferences that researchers usually make. I begin by introducing the concept of Type I error rates. I then illustrate my argument with respect to model misspecification, multiple testing, selective inference, forking paths, exploratory analyses, p-hacking, optional stopping, double dipping, and HARKing. In each case, I demonstrate that relevant Type I error rates are not usually inflated above their nominal level and, in the rare cases that they are, the inflation is easily identified and resolved. I conclude that the replication crisis may be more a crisis of theoretical inference than of statistical inference.