View profile

Mental Models Weekly - Issue #8 - Regression to the Mean

Introducing one of the greats of the discipline of statistics - Sir Francis Galton! He brought us man
Mental Models Weekly
Mental Models Weekly - Issue #8 - Regression to the Mean
By Mental Models Weekly • Issue #8 • View online
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.
Photo by Tom Pottiger on Unsplash
Photo by Tom Pottiger on Unsplash
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.
Spoiler: It's not a jinx, it's regression to the mean
Spoiler: It's not a jinx, it's regression to the mean
👩‍💻 If you want to get a bit more technical, check out this write up from the Royal Statistical Society.
Got comments?
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! 🙇 
Did you enjoy this issue?
Mental Models Weekly

How do the best thinkers think the best?

They install mental models - framework for your thinking. Mental models are fundamentals or first principles anyone can learn, and then call upon to greatly enhance judgement and decision making. These models are often drawn from core disciplines such as philosophy, psychology, physics, chemistry, mathematics etc.

Expand your mind with the timeless tool of mental models. Join me learning a new mental model each week!

If you don't want these updates anymore, please unsubscribe here
If you were forwarded this newsletter and you like it, you can subscribe here
Powered by Revue