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  "body": "\n## Summary\n\nThe efficiency of an unbiased estimator measures how close its variance comes to the smallest possible variance, the Cramér–Rao lower bound.\nAn estimator whose variance attains the bound is called efficient.\nThe relative efficiency of two estimators is the ratio of their variances.\nIt tells how many more observations the worse estimator needs to match the better one.\nAsymptotic versions compare the variances of the limit distributions as the sample size grows.\n\n## Explanations\n\n### L1 human\n\nTwo estimators can both be right on average, but one may scatter less around the truth.\nThe one with smaller scatter uses the information in the data better; it is more efficient.\nFor normal data, the sample mean and the sample median both estimate the centre, but the median has about $1/0.64 \\approx 1.57$ times the variance of the mean in large samples.\nSo the median needs about $57\\%$ more data to be as precise as the mean.\n",
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