This study presents a case for applying a transformation (Box and Cox, 1964) of the criterion used in predictive validity studies. The goals of the transformation were to better meet the assumptions of the linear regression model and to reduce the residual variance of fitted (i.e., predicted) values. Using data for the 2008 cohort of first-time, first-year students attending 129 different colleges and universities, this study demonstrated how the transformation may be incorporated into the process of validating an admission test for predicting students' first-year grade point average (FYGPA). In this setting, we regularly found substantial negative skewness in both the criterion and its residuals and the use of a Box-Cox transformation of the criterion yielded residuals that much better approximated the assumption of normally distributed residuals. The result is that with relatively little additional effort, researchers may perform predictive validity studies for college admission measures with one of the most meaningful criteria (i.e., FYGPA) and they can better meet the assumptions of the linear regression model.