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Published online by Cambridge University Press: 06 July 2010
Summary
The regression fallacy occurs with repeated measures. The normative rule is that when a past average score happens to lie well above or below the true average, future scores will regrees towards the average. Kahneman and Tversky attribute the regression fallacy to the heuristic that future scores should be maximally representative of past scores, and so should not regress. Suppose students are accustomed to seeing individuals vary from time to time on some measure. If so, the majority are likely to recognize regression in individuals on this measure when they are alerted to the possibility. Regression in group scores reduces the correlation between the scores obtained on separate occasions. Taking account of regression in predicting a number of individual scores reduces the accuracy of the predictions of the group scores by reducing the width of the distribution.
To avoid the regression fallacy, people need to have it explained to them. They should beware of the spurious ad hoc explanations that are often proposed to account for regression. Examples are given of regression after an exploratory investigation, during a road safety campaign, following reward and punishment, and during medical treatment for a chronic illness that improves and deteriorates unpredictably.
Regression towards the average
Regression can occur whenever quantities vary randomly. The normative rule is that future scores regress towards the average. Kahneman and Tversky (1973, p. 250) suggest that the heuristic bias is based on the belief that future scores should be maximally representative of past scores, and so should not regress.
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