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One of the most difficult tasks facing industrial-organizational psychologists is evaluating the importance of variables, especially new variables, to be included in the prediction of some outcome. When multiple regression is used, common practices suggest evaluating the usefulness of new variables by showing incremental validity beyond the set of existing variables. This approach assures that the new variables are not statistically redundant with this existing set, but this approach attributes any shared criterion-related validity to the existing set of variables and none to the new variables. More importantly, incremental validity alone fails to answer the question directly about the importance of variables included in a regression model—arguably the more important statistical concern for practitioners. To that end, the current article reviews 2 indices of relative importance, general dominance weights and relative weights, which may be used to complement incremental validity evidence and permit organizational decision makers to make more precise and informed decisions concerning the usefulness of predictor variables. We illustrate our approach by reanalyzing the correlation matrices from 2 published studies.