"Rewarding good performance leads to faster improvement than punishing bad performance"
"In general, unusually bad performance improves after punishment, but good performance tends not to improve and sometimes even gets worse after praise is administered."
These statements seem contradictory, yet both describe real effects. The apparent contradiction is caused by a phenomenon known as "regression to the mean," which states that the measurement after an exceptional measurement will be closer to average. The improvement after a reprimand is caused not by any effect that reprimand had, nor was the worsening after praise due to the praise. Both observations were due to regression to the mean.
Regression to the mean is caused by two things.
1. Exceptionally good performance is far above average, and exceptionally bad performance is far below average.
2. Most performance is about average.
Let's put this in concrete terms. Let's say we are trying to teach our friend Bob to play darts. He's not very good yet, and while he almost always hits the board, he can't really get a higher level of accuracy than that.
On his 12th throw, Bob misses the dartboard entirely. This is extraordinarily bad, even for him. On his next throw, he gets an 8, which is fairly typical, and much better.
On his 57th throw, your friend manages to get a bullseye. You slap him on the back, congratulating him on his improvement. It seems your friend really is getting the hang of this after all. Proud of his accomplishment, Bob lines up his next attempt. He cocks his arm, throws, and...
Gets a 12.
This is regression to the mean. Since there is a large random factor in darts, especially for unskilled players, a good throw will probably not be followed up with another good throw (since good throws are rare, and one shot is independent of another). This effect shows up whenever there is a large random component in performance.
It's important to be aware of when an exceptional observation was likely due to random variation in measurement, not an exceptional characteristic of the thing being measured. For example, stock performance is mostly random. If one of your stocks does extremely well this year, you should expect it to perform much closer to average next year. If your kid scores 160 on his/her first IQ test, you should expect lower performance on later tests. If it took you 3 hours to get to work yesterday because of traffic instead of the usual 1, you shouldn't worry too much about it taking 3 hours again today.
This is a very rough draft of a post on regression to the mean. I wrote it because the sequences didn't cover this particular bias blind spot, and it's an important one. I appreciate any feedback, both in terms of providing examples and making it easy to understand and in terms of cleaning up any errors or awkward phrasings.