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.
I have just put out an article dealing with numerous misconceptions about this topic, and a complete explanation of why autism diagnoses have become more common.
It starts with acknowledging that more kids are diagnosed than in the past:
But this is misleading for a few reasons.
One has to do with how this data was sourced. We didn't have a DSM with autism in it before 1980, so all the oldest people in this cohort were diagnosed as adults.
Adults are underdiagnosed. Go out of your way to diagnose? Same rates.
So something is off about this graph.
A major issue is that the older diagnoses here were done under a more arbitrary criteria: Autism has only been a described thing since Kanner's studies in 1943 and mass diagnosis kicked off in 1980.
In 2016, researchers found that the minority-White wage gap was overestimated by about 10% because, at work, non-Whites tended to partake in more leisure, waiting around, etc.
They delayed releasing the study out of fear Trump would "use it as a propaganda piece."
They explicitly admitted that they let their personal politics get in the way of releasing a study with contentious but correct findings.
That doesn't inspire trust, but at the same time, given the topic, it might!
This isn't the worst example of scientists hurting the public for political reasons.
More infamously, this guy stopped the release of the COVID vaccines to prevent Trump from winning re-election in 2020, killing tens of thousands in the process.
If you want to "fix" this situation within reason, you need to cut funding.
Doing that has disproportionately negative impacts for the educations of people from socioeconomically worse off backgrounds. Or in other words, it hurts upward educational mobility for the poor.
Or, you could provide this presidential administration with a gift:
Centralize the universities and have the government more directly control all the funding. Make them "free".
This is far more likely than alternatives like 'Just give universities infinite money', but still bad
Compared to twenty years ago, kids are eating some types of ultraprocessed foods more and some types lessđź§µ
For example, one thing there's proportionally less of is sugar-sweetened beverage consumption. Meanwhile, there's relatively greater sweet snack consumption.
Overall, the ultraprocessed food (UPF) consumption share is up across young ages to similar degrees.
The increase is definitely there, but it isn't dramatic. For example, going from 61% to 67.5% is an 11% increase in twenty years.
The increase in consumption is not differentiated by the sex of children.
In other words, boys and girls are both eating a bit more ultraprocessed food.