In the two decades since Meehl's (1954) book on the respective accuracy of clinical versus clerical prediction, little practical con- sequence has been observed. Diagnoses are still made by clinicians, not by clerks; college admissions are still done by committee, not by computer. This is true despite the consid- erable strength of Meehl's argument that hu- mans are very poor at combining information optimally and that regression models evi- dently combine information rather well. These points were underlined in some recent work by Dawes and Corrigan (1974), in which they found again that human predictors do poorly when compared with regression mod- els. Strikingly, they found that for some rea- son, linear models with random regression weights also do better than do humans. Even more striking, when all regression weights were set equal to one another they found still higher correlation with criterion on a validat- ing sample. The obvious question here is Why? Is it because humans are so terrible at combining information that almost any rule works better, or is it some artifact of linear regression?