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  "body": "\n## Summary\n\nA decision rule is admissible if no other rule beats it in the following sense.\nThe other rule is at least as good for every value of the parameter and strictly better for some value.\nAdmissibility is a minimal requirement of rationality in [[statistical decision theory|CPFSBBHXT5]]: an inadmissible rule can be replaced by one that never does worse.\n[[Stein's example|7SC9E65XDN]] shows that even very natural estimators can fail to be admissible.\n\n## Explanations\n\n### L1 human\n\nCompare two rules by their average loss for each possible truth.\nIf one rule is never worse than another and sometimes better, the worse one should never be used: it is inadmissible.\nAn admissible rule is one that cannot be beaten in this complete way.\nBeing admissible does not mean being good.\nThe rule that always guesses $0$, ignoring the data, is admissible for estimating a normal mean, because nothing beats it when the truth is $0$.\n",
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