In regression analysis courses, there are many settings in which the response variable under study is continuous, strictly positive, and right skew. This type of response variable does not adhere to the normality assumptions underlying the traditional linear regression model, and accordingly may be analyzed using a generalized linear model assuming either a lognormal or gamma distribution. As such, students oftentimes become confused about which of these two distributions should be chosen to model a given dataset. In this article, we argue that the comparability of these two models should be taught through both simulation and real data analysis. Students will learn to identify the cases in which these two models can be used somewhat interchangeably through this teaching methodology.