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."
"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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