Normal Deviate

Statisticians are woefully ignorant about computer science (CS).

And computer scientists are woefully ignorant about statistics.

O.k. I am exaggerating. Nonetheless, it is worth asking: what important concepts should every statistician know from computer science?

I have asked several friends from CS for a list of the top three things from CS that statisticians should know. While there wasn’t complete agreement, here are the three that came up:

  1. Computational complexity classes. In particular, every statistician should understand what P and NP mean (and why you get $1,000,000 from the Clay Mathematics Institute if you prove that $latex {P\neq NP}&fg=000000$.). Understanding the fact that searching through all submodels in a variable selection problem is NP hard will convince you that solving a convex relaxation (a.k.a. the lasso) is a really good idea.
  2. Estimating computing time. In CS and machine learning, it is expected that one will estimate the number of operations…

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