People are getting thousands of likes for spreading this misinformation about sex differences. Let me explain why this interpretation of the data is wrong. 🧵 If you think 100% accuracy is too good to be true, trust your instincts.

The version of the model shown in the plot was basically fed the sex of the participants. That’s why it’s achieving 100% accuracy.

I keep seeing this Huberman clip all over my timeline so let’s use it as teachable moment to learn some statistics. The basic mistake is not taking the people who are already pregnant out of the pool of people who could be pregnant the next month. Of the starting 100, fewer and fewer will remain each month.

are you always busy but never seem to get enough done? i recently learned a very important lesson about focus: it's extremely powerful when all your projects fall under one overarching goal such that they feed into and enhance each other.

Racist propaganda in the form of statistical charts is extremely effective because most people are unaware of the decisions being made behind the scenes. What is typically presented to us as “the data” has often been meticulously pre-chewed for our easy consumption.

This plot earned a "Wow" from Elon so I guess we need to talk about it. Here's a thread of basic mistakes I see over and over again in these terrible race science plots: 1. Inference using crude or unadjusted rates

In a science experiment, we want to compare two groups that are identical in every single way except for the factor we care about.

I honestly think 99% of US politics can be reduced to this one projection by Pew Research. The desperate attempts to force women to have more kids, the terrified rants about immigration, the absolute panic about being “replaced”. The part of the great replacement theory that’s true is the white population is decreasing as a percentage of the total US population, but it’s a lie that this is somehow brown people’s fault.

We often want to know "What are the chances my hypothesis is true given my data?" The answer depends on the baseline plausibility of different kinds of hypotheses which is usually unknowable.

Frequentist and Bayesian statistics are two responses to this lack of information. In Frequentist statistics, the response is to accept the fundamental lack of information and try our best to proceed without it.

Here's something you might not know. Almost 2000 years before anybody had heard of Newton, Archimedes could do calculus. The only problem was Archimedes was a genius so him being able to do it wasn't of much help to the rest of us.

My sense of tech bros is they tend to simplify philosophical problems until they're solvable. For instance, the whole spectrum of human relationships gets reduced to a "follow" button. They then commodify their simplified solution which makes them rich, and them being rich and powerful makes people think they must have really solved the original problem.

This tweet is rightly being condemned for the disgusting racism it represents, but notice how the poster couches his racism as scientific inquiry. This is increasingly what racism looks like on Twitter. It’s easy to see where he’s going with this. If fertility rates are in any way different then this must mean these two people are different species, with all the implications that might have for who deserves human rights.

Bloom’s Taxonomy is one of the most powerful frameworks I’ve ever encountered. It defines what it means to think deeply. For every concept you know, your level of comprehension can be placed along this spectrum.

“Statistics is unrealistic because statisticians assume everything is normally distributed”

This is wrong but the reason why is incredibly cool. To understand, we need to talk about the multiverse. 👇 A very typical scenario is for a scientist to collect a random sample from a population and then compute a statistic like average height.

Based on the responses to my criticisms of race science, I'm realizing a lot of people misunderstand what a statistical analysis is so here's a brief overview. 👇

Almost every statistical analysis is of this form: A statistical analysis attempts to prove that "A implies B" but it's typically agnostic about whether the assumptions are true. Therefore, it's actually agnostic about whether the conclusions are true as well.

This article argues in part that racism and sexism are essentially non-existent because their effects on stuff like income disappear if you control for all relevant variables like education, work history and so on. This is a common argument so let me explain why it's wrong. 👇

https://twitter.com/NRO/status/1752759994452201545

Social forces like sexism and racism aren't magical. They act through specific mechanisms in the physical world.

FIVE books that can help you learn real analysis (and math in general) 🧵 Problem: Don’t know how to write mathematical proofs.

Solution:

Story time. One time my job decided to pay for everybody to go to the movies. Everybody else wanted to sneak food in but I didn’t because I was like they’re gonna search us. Everybody else was like “Search us??? Why would they do that?”.

Elon, this guy is wrong. Race science is always just bad statistical thinking. 🧵 He's comparing black high school students to professional airline pilots and then concluding it's virtually impossible for black people to be good airline pilots.

The claim that affirmative action completely invalidates the credentials of black folks just seems like more racism to me. Nobody says the gold medal in women’s tennis is completely meaningless just because only women can win it.

For me, this is about fairness. They annihilated Claudine Gay’s reputation over this issue. They said as a scholar she was sickeningly unqualified. They said she brought shame to Harvard. Where is that energy now? Here are the text comparisons if you want to see for yourself.

A lot of these examples of “plagiarism” are pretty debatable to me, but they seem worth discussing so let’s explore a few. The phrase “all previous tax incentives for investing in rental housing of any kind” does not seem worth stealing. It is not clever or unique.

I keep seeing a pattern where people bring up a professor being fired as proof that universities are lying when they say they are legally required to respect student free speech. Here's the thing though: Students have more free speech rights than faculty members (who are employees after all) and faculty members (due to tenure) have more free speech than administrators.