This is an update to my Analysis Philosphy page, which is still working towards completion Nonlinearity is a commonly-misunderstood problem when it comes to data analysis, mostly because our profession has once again managed to find a way to use a simple-sounding term in a way that’s counterintuitive to lay audiences. (See also Artificial Intelligence is Dumb.) When people think about nonlinear response variables, they think of functions that have non-linear relationships.
This is an update to my Analysis Philosphy page, which is still working towards completion I only get 1,750 hits on Google when I search for “Distributionality”, so maybe I should clarify what I mean, though I don’t think it’s anything profound. That data follow distributions is a tautology. When this doesn’t appear the case, it means we’ve failed to properly model hte data generation function. The most typical failure mode is to assume that the distribution is simpler than it is.
Nate Duncan’s “Dunc’d On” is probably my favorite NBA podcast. He and frequent co-host Danny Leroux are analytical and comprehensive, covering the whole league. About every other week, they’ll go through ever team in a conference (East or West) and talk about how each team is doing, where they’re projected to finish, etc. They call these episodes “15 in 60”, although they don’t always get to all 15 teams in the conference, and I don’t think they’ve ever done one of these in 60 minutes.