The following is excerpted from Module 10 of your text.

Dawes (1976) explained why statistical prediction is so often superior to an interviewer's intuition when predicting certain outcomes such as graduate school success:

What makes us think that we can do a better job of selection by interviewing [students] for a half hour, than we can by adding together relevant [standardized] variables, such as undergraduate GPA, GRE score, and perhaps ratings of letters of recommendation. The most reasonable explanation to me lies in our overevaluation of our cognitive capacity. And it is really cognitive conceit. Consider, for example, what goes into a GPA. Because for most graduate applicants it is based on at least 3 1/2 years of undergraduate study, it is a composite measure arising from a minimum of 28 courses and possibly, with the popularity of the quarter system, as many as 50. . . . Yet you and I, looking at a folder or interviewing someone for a half hour, are supposed to be able to form a better impression than one based on 3 1/2 years of the cumulative evaluations of 20-40 different professors. . . . Finally, if we do wish to ignore GPA, it appears that the only reason for doing so is believing that the candidate is particularly brilliant even though his or her record may not show it. What better evidence for such brilliance can we have than a score on a carefully devised aptitude test? Do we really think we are better equipped to assess such aptitude than is the Educational Testing Service, whatever its faults?

1	Do you agree that statistical prediction is superior to an interviewer's intuition when predicting certain outcomes such as school success?
	A)	Yes
	B)	No