Statistical zealots

Yesterday my data sharing policy went a little bit viral. It hit the front page of Hacker News and was a trending repo on Github. I was reading the comments on Hacker News and came across this gem:

So, while I can imagine there are good Frequentists Statisticians out there, I insist that frequentism itself is bogus.

This is the extension of a long standing debate about the relative merits of frequentist and Bayesian statistical methods. It is interesting that I largely only see one side of the debate being played out these days. The Bayesian zealots have it in for the frequentists in a big way. The Hacker News comments are one example, but here are a [Yesterday my data sharing policy went a little bit viral. It hit the front page of Hacker News and was a trending repo on Github. I was reading the comments on Hacker News and came across this gem:

So, while I can imagine there are good Frequentists Statisticians out there, I insist that frequentism itself is bogus.

This is the extension of a long standing debate about the relative merits of frequentist and Bayesian statistical methods. It is interesting that I largely only see one side of the debate being played out these days. The Bayesian zealots have it in for the frequentists in a big way. The Hacker News comments are one example, but here are a](http://wmbriggs.com/blog/?p=5062) more. Interestingly, even the “popular geek press” is getting in the game.

I think it probably deserves a longer post but here are my thoughts on statistical zealotry:

  1. User effect »»»»»»»»> Philosophy effect. The person doing the statistics probably matters more than the statistical philosophy. I would prefer Andrew Gelman analyzed my data than a lot of frequentists. Similarly, I’d prefer that John Storey analyzed my data than a lot of Bayesians. 
  2. I agree with Noahpinion that this is likely mostly a philosophy battle than a real practical applications battle.
  3. I like Rob Kass’s idea that we should move away from frequentist vs. Bayesian to pragmatism. I think most real applied statisticians have already done this, if for no other reason than being pragmatic helps you get things done.
  4. Papers like this one that claim total victory for one side or the other all have one thing in common: they rarely use real data to verify their claims. The real world is messy and one approach never wins all the time.

My final thought on this matter is: never trust people with an agenda bearing extreme counterexamples.