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.
They end up publishing fewer papers and they receive fewer citations.
In other words, scientific productivity fallsđź§µ
Tons of scholars have been cancelled in recent years.
That is, they've received professional backlash for expressing views that people deem "controversial, unpopular, or misaligned with prevailing norms."
Cancellations happen outside of academia, but it's very bad in it.
Large portions of the academy dislike the freedom of speech. Many of those free speech opponents have high agency and the clout to cause material harm to people they dislike = particularly bad cancel culture.
Phenotyping is the vast, minimally-explored frontier in genome-wide association studies.
Important threadđź§µ
Briefly, phenotyping is how you measure people's traits. Measure poorly, get bad results; measure well, get good results.
Example? Janky knees.
The janky knee example refers to osteoarthritis, the most common form of arthritis, which occurs when the cartilage between bones is worn down, so bones start rubbing against each other.
This ends up being very painful.
Everyone with this condition isn't necessarily diagnosed with it.
This is especially true for men, who tend to just ignore this (and many other conditions) more often than women do.
This is, in a word, annoying, because it means that if you study it, sampling is likely biased.