CTK Insights

25 May


Paul Meehl's (1954) book Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence appeared 25 years ago. It reviewed studies indicating that the prediction of numerical criterion variables of psychological interest (e.g., faculty ratings of graduate students who had just obtained a Ph.D.) from numerical predictor variables (e.g., scores on the Graduate Record Examination, grade point averages, ratings of letters of recommendation) is better done by a proper linear model than by the clinical intuition of people presumably skilled in such prediction. The point of this article is to review evidence that even improper linear models may be superior to clinical predictions.

The most important development in the field since Meehl's original work is Robyn Dawes famous article.

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.

According to Meehl, there are few circumstances under which it is a good idea to substitute judgment for a formula. In a famous thought experiment, he described a formula that predicts whether a particular person will go to the movies tonight and noted that it is proper to disregard the formula if information is received that the individual broke a leg today. The name "broken-leg rule" has stuck. The point, of course, is that broken legs are very rare — as well as decisive.


  1. Robyn M. Dawes, The robust beauty of improper linear models in decision making, in Judgement under uncertainty: Heuristics and biases, Cambridge University Press; 1 edition (April 30, 1982)
  2. Daniel Kahneman, Thinking, fast and slow, Farrar, Straus and Giroux (October 25, 2011)

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