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The authors quantify the conventional wisdom that predictors’ correlations with cognitive ability are positively related to subgroup mean differences. Using meta-analytic and large-N data from diverse predictors, they found that cognitive saturation correlates .84 with predictors’ artifact-corrected Black–White d values and .95 with predictors’ artifact-corrected Hispanic–White d values. The authors also investigate the extent to which d values are associated with the use of assessor-based scoring and with predictor domains in which differential investment is likely to occur. As a practical application of these findings, they present a procedure to forecast mean differences on a new predictor based on its cognitive saturation and other attributes. They also present a Bayesian framework that allows one to integrate regression-based forecasts with observed d values to achieve more precise estimates of mean differences. The proposed forecasting techniques based on the relationship between mean differences and cognitive saturation can help to mitigate the difficulties inherent in computing precise local estimates of mean differences.