Factors affecting the accuracy of predictions, regardless of method, are discussed: measurement reliability, base rates, selection ratios, sample representativeness, and cross validation. Research suggests that predictions of decisions are more accurate than are predictions of behavior. In addition, statistical predictions outperform intuitive or clinical predictions. The following methods of prediction are described: multiple regression, the Burgess method, clustering models, and multidimensional contingency table analysis. The article concludes that predictive accuracy can be enhanced by improving data reliability and measurement techniques, by employing both statistical and subjective bootstrapping models, and by using theory-driven approaches. Finally, investigators should pay more attention to ethical considerations. Approximately 70 references. (Author abstract modified)