An article named “This is how we do modern frequentist statistics” is an excerpt from a book called “Bayesian workflows”

Where is the article explaining that?

Anyway, great article, thanks for sharing.

From your description, I thought “wait, is this going to be Andrew Gelman?”

It is.

(For context: https://youtu.be/ZmbrsbYwRWw )

Wait a bayesian advocate using a frequentist approach and not having a fight over which branch is supreme. What's happening with the world. I guess all stats united against AI.

Gelman (2018, and reiterated 2024): "Bayesians are frequentists." (https://statmodeling.stat.columbia.edu/2018/06/17/bayesians-...)

As mentioned in the sibling comments, Andrew Gelman has covered this elsewhere. In particular, Gelman et al have a "Model Checking" chapter in their Bayesian Data Analysis book https://sites.stat.columbia.edu/gelman/book/BDA3.pdf . A popular intro Bayesian book Statistical Rethinking has a similar "Sampling from the Imaginary" chapter (https://civil.colorado.edu/~balajir/CVEN6833/bayes-resources...). Both books introduce the topic early (the latter book deals with it in the third chapter) if you are willing to do a bit of reading.

This should be automatic now in any statistical analysis given ubiquity of coding agents.

Where did he get the attractivess distribution, from the paper??

You were just one click away from finding out:

"Figure 2. Using a sample of 2,972 respondents from the National Longitudinal Study of Adolescent Health, each of whom had been rated on a five-point scale of attractiveness […]"

This technique is very useful to gain intuition for a given sample size. Just run a few simulations with uncorrelated data and then you can get a sense of how extreme the estimators can be.