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  "body": "\n## Summary\n\nA minimum-variance unbiased estimator is an unbiased estimator with the smallest variance among all unbiased estimators.\nIt must be smallest at every value of the parameter.\nIt is also called a uniformly minimum-variance unbiased estimator (UMVUE).\nIt need not exist, but when it does it is unique with probability one.\nThe Rao–Blackwell and Lehmann–Scheffé theorems construct it from a complete sufficient statistic.\n\n## Explanations\n\n### L1 human\n\nAmong all rules that are right on average, pick the one that scatters least around the truth, whatever the truth is.\nThat rule is the minimum-variance unbiased estimator.\nFor coin flips, the proportion of heads is such a rule for the probability of heads.\nThe requirement \"whatever the truth is\" is strong: a rule that is best for one value of the parameter but worse for another does not qualify.\n",
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