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.
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 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.
"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 […]"
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 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.
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 should be automatic now in any statistical analysis given ubiquity of coding agents.