This is what I compulsively read Gelman for:

"The models in their book are qualitative, all about the directions of causal arrows...

Statistical inference & machine learning focus on the quantitative: the relationship between measurements and the underlying constructs; the relationships between different quantitative variables; time-series and spatial models; the causal effects of treatments, and treatment interactions; and we model variation in all these things."

[1/3]

"Both the qualitative and the quantitative are necessary... [but stats is] usually all about how to get the answer given the assumptions, and not enough about where the assumptions come from...

If all you do is set up probability models, without thinking seriously about their connections to reality, then you’ll be missing a lot..."

"If you think you’re working with a purely qualitative model, it turns out that, no, you’re actually making lots of data-based quantitative decisions about which effects and interactions you decide are real and which ones you decide are not there. And if you think you’re working with a purely quantitative model, no, you’re really making lots of assumptions (causal or otherwise) about how your data connect to reality."

A Mastodon instance for maths people. The kind of people who make $\pi z^2 \times a$ jokes.

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