Stein's paradox and batting averages

A simple explanation of Stein's paradox through the famous baseball example of Efron and Morris

Samuel Perreault

9 minutes read

There is nothing from my first stats course that I remember more clearly than Prof. Asgharian repeating “I have seen what I should have seen” to describe the idea behind maximum likelihood theory. Given a family of models, maximum likelihood estimation consists of finding which values of the parameters maximize the probability of observing the dataset we have observed. This idea, popularized in part by Sir Ronald A. Fisher, profoundly changed the field of statistics at a time when…

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