Regression to the Mean

intermediate2 min readbusiness · economics · psychology · statistics

Regression to the mean occurs when unusually high or low observations tend to be followed by less extreme ones in an imperfectly correlated process. The bias is in overlooking this tendency or mistaking it for the effect of praise, criticism, or another intervention.

THE IDEA

SPEAKING OF REGRESSION TO MEDIOCRITY

“She says experience has taught her that criticism is more effective than praise. What she doesn’t understand is that it’s all due to regression to the mean.”

“Perhaps his second interview was less impressive than the first because he was afraid of disappointing us, but more likely it was his first that was unusually good.”

“Our screening procedure is good but not perfect, so we should anticipate regression. We shouldn’t be surprised that the very best candidates often fail to meet our expectations.”

Taming Intuitive Predictions (Chapter 18)

Life presents us with many occasions to forecast. Economists forecast inflation and unemployment, financial analysts forecast earnings, military experts predict casualties, venture capitalists assess profitability, publishers and producers predict audiences, contractors estimate the time required to complete projects, chefs anticipate the demand for the dishes on their menu, engineers estimate the amount of concrete needed for a building, fireground commanders assess the number of trucks that will be needed to put out a fire. In our private lives, we forecast our spouse’s reaction to a proposed move or our own future adjustment to a new job.

Some predictive judgments, such as those made by engineers, rely largely on look-up tables, precise calculations, and explicit analyses of outcomes observed on similar occasions. Others involve intuition and System 1, in two main varieties. Some intuitions draw primarily on skill and expertise acquired by repeated experience. The rapid and automatic judgments and choices of chess masters, fireground commanders, and physicians that Gary Klein has described in Sources of Power and elsewhere illustrate these skilled intuitions, in which a solution to the current problem comes to mind quickly because familiar cues are recognized.

Other intuitions, which are sometimes subjectively indistinguishable from the first, arise from the operation of heuristics that often substitute an easy question for the harder one that was asked. Intuitive judgments can be made with high confidence even when they are based on nonregressive assessments of weak evidence. Of course, many judgments, especially in the professional domain, are influenced by a combination of analysis and intuition.

When to use it

When assessing the impact of a new management strategy after an unusually good quarter.
In medical trials, accounting for regression to the mean when measuring treatment effects on patients with extreme baseline values.
In sports, predicting a player's future performance after a record-breaking season.
When evaluating student test scores after an unusually high or low performance.

FROM THEORY TO EVERYDAY LIFE

In practice

Criticism vs Praise

She says experience has taught her that criticism is more effective than praise. What she doesn’t understand is that it’s all due to regression to the mean.

Second Interview

Perhaps his second interview was less impressive than the first because he was afraid of disappointing us, but more likely it was his first that was unusually good.

Screening Procedure

Our screening procedure is good but not perfect, so we should anticipate regression. We shouldn’t be surprised that the very best candidates often fail to meet our expectations.

✳

A MOMENT TO REFLECT

Put it to work.

Think of a situation where you observed an extreme outcome followed by a more average one. Could regression to the mean be a factor?
When evaluating the effectiveness of an intervention (like praise or criticism), how can you account for regression to the mean?
How might regression to the mean affect your interpretation of performance metrics over time?