Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new insights into the causes of this problem. Computer simulations and an illustrative example are used to demonstrate that when the predictor variables in a multiple regression model are correlated and one or more of them contains random measurement error, Type I error rates can approach 1.00, even for a nominal level of 0.05. The most important factors causing the problem are summarized and the implications are discussed. The authors use Zumbo’s Draper–Lindley–de Finetti framework to show that the inflation in Type I error rates results from a mismatch between the data researchers have, the assumptions of the statistical model, and the inferences they hope to make.