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https://en.wikipedia.org/wiki/Berkson%27s_paradox is also one to be aware of. There are lots of ways for error to creep in when populations are created in a biased way. These two effects explain a lot of the stupid decisions that come out of "data driven" processes. It is common for data to suggest the opposite of the truth.
> Mathematician Jordan Ellenberg argues that Simpson's paradox is misnamed as "there's no contradiction involved, just two different ways to think about the same data" and suggests that its lesson "isn't really to tell us which viewpoint to take but to insist that we keep both the parts and the whole in mind at once."
Keeping multiple possibilities in mind at once was what allowed the Epicureans to determine survival of the fittest, trait inheritance from each parent, that light was made of discrete units that weighed very little and were moving very fast, and that in order for free will to exist the quanta making up matter had to have multiple possible results under the same governing physical laws and conditions - all several millennia before the scientific method independently found the same results.
It's a great analytical method, especially in data analysis as suggested here.
https://en.wikipedia.org/wiki/Berkson%27s_paradox is also one to be aware of. There are lots of ways for error to creep in when populations are created in a biased way. These two effects explain a lot of the stupid decisions that come out of "data driven" processes. It is common for data to suggest the opposite of the truth.
Of course, an empiricist can just point to https://en.wikipedia.org/wiki/Goodhart%27s_law
It may be true in some cases, but I think the "Any" is disproven by the existence of proper scoring rules.
I once encountered this in the real world as a data analyst a long time ago. I was working at an e-commerce company, called The Hut Group, and the whole year our marketing team had been saying our marketing cost of goods sold (the percentage of our revenue we needed to spend on marketing) had been declining across every product category. But at year end, the execs were shocked to realize that our cost of goods sold h…
At the time, the average price for single-family house sales was higher for houses without central AC than for houses with central AC. Yet when you split the data down by state, in every state the relationship was reversed: houses with central AC were more expensive than houses without.
The higher nationwide average price of houses without central AC was driven primarily by the large number of expensive houses in California without central AC.
Imagine the horizontal axis is dose of a drug, and the vertical is the response, like hours of sleep. Looking at Lisa's response, it's clear that increasing the dose reduces sleep. Same for Bart. But if you do a linear regression of all the data, shown by the red line, dose increases sleep, which is wrong.
Earlier quoted context omitted.
Of course, an empiricist can just point to https://en.wikipedia.org/wiki/Goodhart%27s_law
> Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes. It may be true in some cases, but I think the "Any" is disproven by the existence of proper scoring rules.
Surprised at the similarity but of course that was probably on purpose by the genius Simpsons writers of the late 90's.
Upon first glance I assumed it was the Simpson's episode about Mr. Burns having "a vast range of diseases so great in fact that they cancel each other out," explained here: https://simpsons.fandom.com/wiki/Three_Stooges_Syndrome Surprised at the similarity but of course that was probably on purpose by the genius Simpsons writers of the late 90's.