Prediction with single event versus aggregate data

The impact of statistical information on predictive judgment was studied in a cue probability learning (CPL) task. Two kinds of aggregate information about criterion events were used: the conditional mean (Z_M) and the conditional interquartile range (Z_R). The single event information was the exact criterion value for one randomly selected past case (Z_O). Results showed that Z_Mand Z_R increased prediction consistency and accuracy and reduced bias while Z_O led to more appropriate cue weighting but lower consistency and accuracy. When Z_M, Z_R, and Z_O were all provided, the unique benefits of single-event and aggregate data were combined. This was true even for subjects without any prior task experience. When able to select only one of Z_M, Z_R, and Z_O, judges most often chose aggregate information, particularly Z_R. However, the statistical information was underutilized. Recommendations for aiding predictive judgment are made.