This Digest presents a discussion of the assumptions of multiple regression that is tailored to the practicing researcher. The focus is on the assumptions of multiple regression that are not robust to violation, and that researchers can deal with if violated. Assumptions of normality, linearity, reliability of measurement, and homoscedasticity are considered. Checking these assumptions carries significant benefits for the researcher, and making sure an analysis meets the associated assumptions helps avoid Type I and II errors. Attending to such issues as attenuation due to low reliability, curvilinearity, and nonnormality often boosts effect sizes, usually a desirable outcome. There are many nonparametric statistical techniques available to researchers when the assumptions of a parametric statistical technique are not met. These are often somewhat lower in power than parametric techniques, but they provide valuable alternatives for researchers. (SLD)