There are people who desperately want this to be untrueđź§µ
One example of this came up earlier this year, when a "Professor of Public Policy and Governance" accused other people of being ignorant about SAT scores because, he alleged, high schools predicted college grades better.
The thread in question was, ironically, full of irrelevant points that seemed intended to mislead, accompanied by very obvious statistical errors.
For example, one post in it received a Community Note for conditioning on a collider.
But let's ignore the obvious things. I want to focus on this one: the idea that high schools explain more of student achievement than SATs
The evidence for this? The increase in R^2 going from a model without to a model with high school fixed effects
This interpretation is bad.
The R^2 of the overall model did not increase because high schools are more important determinants of student achievement. This result cannot be interpreted to mean that your zip code is more important than your gumption and effort in school.
If we open the report, we see this:
Students from elite high schools and from disadvantaged ones receive similar results when it comes to SATs predicting achievement. If high schools really explained a lot, this wouldn't be the case.
What we're seeing is a case where R^2 was misinterpreted.
The reason the model R^2 blew up was because there's a fixed effect for every high school mentioned in this national-level dataset
That means that all the little differences between high schools are controlled—a lot of variation!—so the model is overfit, explaining the high R^2
This professor should've known better for many reasons.
For example, we know there's more variation between classrooms than between school districts when it comes to student achievement.
World War I devastated Britain and likely slowed down its technological progressđź§µ
The reason being, the youth are the engine of innovation.
Areas that saw more deaths saw larger declines in patenting in the years following the war.
To figure out the innovation effects of losing a large portion of a generation's young men who were just coming into the primes of their lives, the authors needed four pieces of data.
The first were the numbers and pre-war locations of soldiers who died.
The next components were the numbers and locations of patent filings.
If you look at both graphs, you see obvious total population effects. So, areas must be normalized.
You know how most books on Amazon are AI slop now? If you didn't, look at the publication numbers.
Compare those to the proportion Pangram flags as AI-generated. It's fully aligned with the implied numbers based on the rise over 2022 publication levels!
Similarly, the rise of pro se litigants has come with a rise in case filings detected as being AI-generated, and with virtually zero false-positives before AI was around.
For reference, the French Revolution ushered in a number of egalitarian laws.
A major example of these had to do with inheritance, and in particular with partibility.
In some areas of France, there was partible inheritance, and in others, it was impartible.
Partible inheritance refers to inheritance spread among all of a person's heirs, sometimes including girls, sometimes not.
Impartible inheritance on the other hands refers to the situation where the head of an estate can nominate a particular heir to get all or a select portion.
In terms of their employment, religion, and sex, people who joined the Nazi party started off incredibly distinct from the people in their communities.
It's only near the end of WWII when they started resembling everyday Germans.
Early on, a lot of this dissimilarity is due to hysteresis.
Even as the party was growing, people were selectively recruited because they were often recruited by their out-of-place friends, and they were themselves out-of-place.
It took huge growth to break that.
And you can see the decline of fervor based on the decline of Nazi imagery in people's portraits.
And while this is observed by-and-large, it's not observed among the SS, who had a consistently higher rate of symbolic fanaticism.
I simulated 100,000 people to show how often people are "thrice-exceptional": Smart, stable, and exceptionally hard-working.
I've highlighted these people in red in this chart:
If you reorient the chart to a bird's eye view, it looks like this:
In short, there are not many people who are thrice-exceptional, in the sense of being at least +2 standard deviations in conscientiousness, emotional stability (i.e., inverse neuroticism), and intelligence.
To replicate this, use 42 as the seed and assume linearity and normality
The decline of trust is something worth caring about, and reversing it is something worth doing.
We should not have to live constantly wondering if we're being lied to or scammed. Trust should be possible again.
I don't know how we go about regaining trust and promoting trustworthiness in society.
It feels like there's an immense level of toleration of untrustworthy behavior from everyone: scams are openly funded; academics congratulate their fraudster peers; all content is now slop.
What China's doing—corruption crackdowns and arresting fraudsters—seems laudable, and I think the U.S. and other Western nations should follow suit.
Fraud leads to so many lives being lost and so much progress being halted or delayed.