The Controversy of Clinical Versus Actuarial Prediction

Topics: Prediction, Regression analysis, Scientific method Pages: 6 (2029 words) Published: February 5, 2009
Accepting Error is Reducing Error:
The Controversy of Clinical versus Actuarial Prediction
One of the important goals of psychology is predicting future events or behaviour. About 40 years ago, Paul Meehl, a famous psychologist, raised an important question about how we should predict future behaviour in his paper with the catchy title “When Shall We Use Our Heads Instead of the Formula?” (268). The “head” in the title of the paper refers to clinical prediction. In clinical prediction, psychologists use their clinical experience to formulate a prediction based on interview impressions, history data and test scores (Meehl, Clinical versus Statistical 4). The “formula” in the title refers to statistical or actuarial prediction. In actuarial prediction, clergies access a chart or table which gives the statistical frequencies of behaviours (“Actuarial Prediction”). Advocates of the clinical method say that clinical prediction is “dynamic, meaningful and sensitive” but actuarial prediction is “mechanical, rigid and artificial” (Meehl, Clinical versus Statistical 4). On the other hand, advocates of the actuarial method claim that actuarial method is “empirical, precise and objective” but clinical prediction is “unscientific, vague and subjective” (Meehl, Clinical versus Statistical 4). The controversy of clinical versus actuarial judgement is not limited to the field of psychology; it also affects education in terms of predicting school performance, criminal justice system in terms of parole board decisions and business in terms of personnel selection. Although this controversy can be traced back half a century ago, social scientists today are still asking: Which of the two methods works better? Can we view any prediction dichotomously as either clinical or actuarial? And, if actuarial predictions are more accurate, should we abandon clinical predictions all together? On one side of the controversy, some people feel that using mere numbers to determine whether students can enter graduate schools or whether prisoners should be released is dehumanizing (Meehl, “Causes and Effects” 374). In her book about social psychology, Thompson describes a young woman who complains that it is horribly unfair that she has been rejected by the Psychology Department at University of California on the bases of mere numbers, without even an interview (88). When my psychology teacher surveyed our class on this issue, about 20 percent of students believe that it is unethical to make predictions based on mere numbers (Brenner). The crux of this ethical concern lies on the belief that each individual is so unique that rigid statistics or equations cannot make the correct prediction in every single case. Indeed, most psychologists agree that rigid statistics are not sensitive to special cases. Paul Meehl’s well-known “broken-leg” example illustrates how “the special powers of the clinician” can predict behaviours more accurately in some special cases: If a sociologist were predicting whether Professor X would go to the movies on a certain night, he might have an equation involving age, academic specialty, and introversion score. The equation might yield [a very high probability] that Professor X will go to the movie tonight. But if…Professor X had just broken his leg and he is in a hip cast that won’t fit in a theatre seat, no sensible sociologist would stick with the equation. (Clinical versus Statistical 24-25) Essentially, it is very important for clinicians to detect the characteristics of each unique individual and make predictions accordingly because clinicians deal with individual cases; they make predictions for each unique individual, not for a group of people. Thus, it is “the individual case that defines the clinician” (Meehl, Clinical versus Statistical 25). Because of the insensitivity of statistics to special cases and the importance of predicting individual cases, many psychologists argue that statistics simply...
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