8/10/12 - statistics vs. judgment

In today's excerpt - in his book Clinical vs. Statistical Prediction: A The­oretical Analysis and a Review of the Evidence, psychoanalyst Paul Meehl gave evidence that statistical models almost always yield better predictions and diagnoses than the judgment of trained professionals. In fact, experts frequently give different answers when presented with the same information within a matter of a few minutes:

"In the slim volume that he later called 'my disturbing little book,' [Paul] Meehl reviewed the results of 20 studies that had analyzed whether clinical pre­dictions based on the subjective impressions of trained professionals were more accurate than statistical predictions made by combining a few scores or ratings according to a rule. In a typical study, trained counselors pre­dicted the grades of freshmen at the end of the school year. The counselors interviewed each student for forty-five minutes. They also had access to high school grades, several aptitude tests, and a four-page personal state­ment. The statistical algorithm used only a fraction of this information; high school grades and one aptitude test. Nevertheless, the formula was more accurate than 11 of the 14 counselors. Meehl reported generally similar results across a variety of other forecast outcomes, including violations of parole, success in pilot training, and criminal recidivism.

"Not surprisingly, Meehl's book provoked shock and disbelief among clinical psychologists, and the controversy it started has engendered a stream of research that is still flowing today, more than fifty years after its publication. The number of studies reporting comparisons of clinical and statistical predictions has increased to roughly two hundred, but the score in the contest between algorithms and humans has not changed. About 60% of the studies have shown significantly better accuracy for the algo­rithms. The other comparisons scored a draw in accuracy, but a tie is tanta­mount to a win for the statistical rules, which are normally much less expensive to use than expert judgment. No exception has been convinc­ingly documented.

"The range of predicted outcomes has expanded to cover medical vari­ables such as the longevity of cancer patients, the length of hospital stays, the diagnosis of cardiac disease, and the susceptibility of babies to sudden infant death syndrome; economic measures such as the prospects of success for new businesses, the evaluation of credit risks by banks, and the future career satisfaction of workers; questions of interest to government agencies, including assessments of the suitability of foster parents, the odds of recidivism among juvenile offenders, and the likelihood of other forms of violent behavior; and miscellaneous outcomes such as the evaluation of scientific presentations, the winners of football games, and the future prices of Bor­deaux wine. Each of these domains entails a significant degree of uncer­tainty and unpredictability. We describe them as 'low-validity environments.' In every case, the accuracy of experts was matched or exceeded by a simple algorithm.

"As Meehl pointed out with justified pride thirty years after the publica­tion of his book, 'There is no controversy in social science which shows such a large body of qualitatively diverse studies coming out so uniformly in the same direction as this one.' ...

"Why are experts inferior to algorithms? One reason, which Meehl suspected, is that experts try to be clever, think outside the box, and consider complex combinations of features in making their predictions. Complexity may work in the odd case, but more often than not it reduces validity. Simple combinations of features are better. Several studies have shown that human decision makers are inferior to a prediction formula even when they are given the score suggested by the formula! They feel that they can overrule the formula because they have additional information about the case, but they are wrong more often than not. ...

"Another reason for the inferiority of expert judgment is that humans are incorrigibly inconsistent in making summary judgments of complex information. When asked to evaluate the same information twice, they frequently give different answers. The extent of the inconsistency is often a matter of real concern. Experienced radiologists who evaluate chest X-rays as 'normal' or 'abnormal' contradict themselves 20% of the time when they see the same picture on separate occasions. A study of 101 indepen­dent auditors who were asked to evaluate the reliability of internal corpo­rate audits revealed a similar degree of inconsistency. A review of 41 separate studies of the reliability of judgments made by auditors, pathologists, psy­chologists, organizational managers, and other professionals suggests that this level of inconsistency is typical, even when a case is reevaluated within a few minutes. Unreliable judgments cannot be valid predictors of anything."


Daniel Kahneman


Thinking Fast and Slow


Farrar, Straus, and Giroux


Copyright 2011 by Daniel Kahneman


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