How to shoot yourself in the foot with various statistical philosophies

I’ve long been a fan of “How to shoot yourself in the foot” jokes. Having shot myself in the foot with different programming languages — particularly with C — I was thinking about how one might shoot oneself in the foot with various statistical approaches. So, here we go…


Subjective Bayesianism (de Finetti): Believe you’ve shot yourself in the foot. Or not. Don’t tell me what to believe!

Objective Bayesianism (Jeffreys): Build the uniquely-appropriate gun for shooting yourself in the foot. Oh, you want to shoot your left foot? That’s a different gun.

Fisherian p values (Fisher): Buy a gun to shoot yourself in the foot. You don’t know why it works, and the salesperson can’t tell you, but everyone uses this gun. Shoot yourself in the hand.

Frequentism (Neyman): Build a gun that, when you pull the trigger over and over, more often than not will shoot yourself in the foot. You can pull the trigger, but you can’t say whether you’ve shot yourself in the foot.

Likelihoodism (Edwards): Borrow a gun from a Bayesian.

And finally, because it all started with programming languages….

How to shoot yourself in the foot with R: Post on R-help. Someone there will be happy to shoot you.

If you have more, post them in the comments!

6 thoughts on “How to shoot yourself in the foot with various statistical philosophies

  1. Design-based inference (survey sampling): Sample n=1 foot at random. To estimate the total pain you'd feel if you shot all N=2 feet in your finite population, shoot the sampled foot N/n=2 times.

  2. Information-Theoretic (AIC): Pull the trigger. Measure the intensity of screaming with a sound meter. Don't pull the trigger. Measure the intensity of screaming with a sound meter. Talk about "relative support for a foot being shot" but have no clue as to whether or not anyone has been shot.

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