Why Praise Seems to Backfire and Scolding Seems to Work

Daniel Kahneman, psychologist and Nobel laureate in Economics, recounts in his book Thinking, Fast and Slow (2011) one of the episodes he himself describes as “one of the greatest satisfactions” of his entire career, precisely because it arose from an objection that, at first, seemed to completely undermine what he was teaching.

The flight instructor’s objection

Kahneman was training instructors from the Israeli Air Force on the psychology of effective training. His message was the standard one in behavioral research at the time: rewards for improvement tend to be more effective than punishments for mistakes, an idea backed by decades of studies with pigeons, rats, and people.

At the end of his talk, one of the more veteran instructors raised his hand with a specific objection, based on his own experience:

• When he praised a cadet for executing an aerobatic maneuver well, on the next attempt they usually did worse.
• When, by contrast, he yelled at a cadet over the radio after a poor execution, on the next attempt they usually improved.

For the instructor, the conclusion was obvious: the theory was wrong, punishment worked, and praise didn’t. Kahneman describes that moment as a revelation: the instructor was entirely right about what he’d observed, and at the same time completely wrong about the explanation he gave for it.

What was really happening: regression to the mean

What the instructor had detected, without knowing it, was a statistical phenomenon called regression to the mean. When a cadet’s performance on a maneuver depends partly on skill and partly on chance (a momentary streak of good or bad luck), an especially good result tends to be followed, on the next attempt, by a somewhat worse one, and an especially bad result tends to be followed by a somewhat better one. Not because there’s some compensating force at work, but simply because the extreme, by definition, is unlikely to repeat itself twice in a row.

The instructor only praised a cadet when their execution had been better than usual, so it was statistically expected that the next attempt would be somewhat worse, regardless of the praise. And he only yelled when the execution had been especially bad, so it was expected that the next attempt would improve, also regardless of the scolding. The instructor had confused a random fluctuation with a cause-and-effect relationship.

The improvised demonstration with two coins

Aware that a purely statistical explanation wouldn’t convince the room, Kahneman turned to an on-the-spot physical demonstration: he drew a target on the floor with chalk and asked each officer present to toss two coins in a row toward it with their back turned, without looking.

The pattern that showed up on the board was exactly the same one the instructor had described with the aerobatic maneuvers:

• Most of those who landed closer to the center on the first toss did worse on the second.
• Most of those who missed more on the first toss improved on the second.

And in this case it was obvious that neither praise nor scolding had anything to do with it, because no one was praising or yelling at anyone between one toss and the next: the only possible explanation was regression to the mean.

A pattern that also shows up in golf

Kahneman illustrates the same principle with a different example: a high-level golf tournament where the average first-day score is 72 strokes. A player who scores 66 on the first day (well above average) probably has real talent, but it’s also reasonable to think that, on top of that, they had an above-average streak of luck that day. The reverse, symmetrically, applies to a player who scores 77.

The most reasonable prediction for the second day isn’t that both will exactly repeat their first-day result, but that both will move somewhat closer to the average: the one who did very well will probably keep doing well, but somewhat worse than the first time, because it’s unlikely their streak of luck will repeat just as intensely; and the one who did poorly will probably improve, not because they’ve “learned their lesson,” but because their bad streak isn’t expected to continue either.

Why this matters beyond sports or flying

Kahneman draws an uncomfortable conclusion from this episode about how feedback works in everyday life: since we tend to praise others when their behavior pleases us and reprimand them when it doesn’t, and since other people’s performance naturally fluctuates around its true average, we end up, in his own words, “statistically punished for being nice and rewarded for being nasty.” Not because scolding genuinely works better than praise, but because the very design of when we praise and when we reprimand makes regression to the mean work almost always in the scolder’s favor.

This same kind of error, attributing a cause to what’s actually a random fluctuation, shows up often in how a team’s or a person’s performance at work gets interpreted, something relevant to any leadership style that leans on positive or negative reinforcement as a management tool, and to anyone with a more analytical profile when interpreting one-off swings in their own or someone else’s performance.