Bayesian analysis of Slate Star Codex survey data.

[Epistemic status: I’m teaching myself Bayesian analysis out of an O’Reilly-esque programming book; I haven’t yet mustered myself to crack the intimidating Andrew Gelman tome on my shelf. I beg you, correct me if I have screwed this up.]

Scott Alexander posted his survey data results several months ago, and recently has been posting some interesting things about how different groups perceive optical illusions.

As part of my quest to finally understand the differences between Bayesian analysis and frequentist analysis, I downloaded his data and poked at it with PyMC, again modeling my analyses after those in chapter 2 of Bayesian Methods for Hackers, by Cameron Davidson-Pilon (the A/B testing example and the Challenger example.)

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