Cross-validated classification accuracies were compared under assumptions of equal and varying degrees of unequal prior probabilities of group membership for 24 bootstrap and 48 simulated data sets. The data sets varied in sample size, number of predictors, relative group size, and degree of group separation. Total-group hit rates were used to compare the relative accuracies across six assumptions about prior probabilities. Contrary to expectations, use of population priors did not always yield the highest hit rate. When group sizes were similar, equal priors yielded greater classification accuracy than sample estimated priors. Results suggest that, when group sizes are similar, use of unequal priors may lead to a decrement in classification accuracy, even with knowledge of population priors. (Contains 5 tables and 13 references.) (Author)