Regression to the Mean
Introducing one of the greats of the discipline of statistics - Sir Francis Galton! He brought us many crucial concepts including today’s mental model - Regression to the Mean (RTM).
This one fools many on a daily basis, and can feel a little counterintuitive at times - which makes it all the more important to internalize into your personal latticework of mental models! 🧠
What is Regression to the Mean?
The phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement.
Huh? What does that mean?
To understand this, let's look at Sir Galton's original study in 1886 - which compared heights of children to their parents. He measured the heights of hundreds of children who'd reached adulthood and the average height of their parents. He was surprised by what he saw when he plotted the data:
It appeared from these experiments that the offspring did not tend to resemble their parents in size, but always to be more mediocre than they – to be smaller than the parents, if the parents were large; to be larger than the parents, if the parents were very small.
Galton originally called this phenomenon "regression towards mediocrity” (if you also have a sense of humor on the dry side this might make you smile - but I digress) - it's now what's commonly termed regression to the mean.
🏀 A memorable example: Michael Jordan's "modestly talented" sons
If you haven't quite wrapped your head around RTM just yet, just try to remember this (slightly dated but still relevant) example from basketball.
It was told well by Razib Khan:
Michael Jordan, the greatest basketball player in the history of the professional game, has two sons who are modest talents at best. The probability that either will make it to a professional league seems low, a reality acknowledged by one of them.
It is still noteworthy of course that both had the talent to make it onto a roster of a Division I NCAA team. This is not typical for any young man walking off the street. But the range in realized talent here is notable.
RTM reminds us that things tend to even out over time!
Want to go deeper?
🏆 The Sports Illustrated Jinx is well worth reading about... The jinx suggests that a super successful player or team who gets featured on the cover Sports Illustrated will then suffer some bad luck as a result! I hope by now you're thinking "but what about plain old regression to the mean?!"
Athletes who are featured on the cover of Sports Illustrated are generally doing extremely well, and so it's more likely their next performance will go down, not up! Check out SI's examples here and a huge list here.
👩💻 If you want to get a bit more technical, check out this write up from the Royal Statistical Society.
Reply to this email if you have any thoughts you want to share.
Wishing you a great week observing occurrences of regression to the mean my dear Mental Modelers! 🙇