Matching Predictions (Nonregressive Predictions)

Correcting Predictions

Matching predictions are errors caused by our reliance on the intuitive matching process. We make matching predictions when we rely on the information we have to make a forecast and behave as if this information were perfectly (or very highly) predictive of the outcome.

Recall the example of Julie, who could "read fluently when she was four years old." The question was, what is her GPA? If you predicted 3.8 for Julie's college GPA, you intuitively judged that the four-year-old Julie was in the top 10% of her age group by reading age (although not in the top 3–5%). You then, implicitly, assumed that Julie would also rank somewhere around the 90th percentile of her class in terms of GPA. This corresponds to a GPA of 3.7 or 3.8—hence the popularity of these answers.

What makes this reasoning statistically incorrect is that it grossly overstates the diagnostic value of the information available about Julie. A precocious four-year-old does not always become an academic overachiever (and, fortunately, a child who initially struggles with reading will not languish at the bottom of the class forever).

More often than not, in fact, outstanding performance will become less outstanding. Conversely, very poor performance will improve. It is easy to imagine social, psychological, or even political reasons for this observation, but reasons are not required. The phenomenon is purely statistical. Extreme observations in one direction or the other will tend to become less extreme, simply because past performance is not perfectly correlated with future performance. This tendency is called regression to the mean (hence the technical term nonregressive for matching predictions, which fail to take it into account).

To put it quantitatively, the judgment you made about Julie would be correct if reading age were a perfect predictor of GPA, that is, if there were a correlation of 1 between the two factors. That is obviously not the case.

The correction is to move a prediction toward the relevant average according to how imperfectly the available information predicts the outcome. Figure 19, which illustrates the procedure in the book, is not included here.