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  "body": "\n## Summary\n\nThe jackknife is a resampling method that recomputes a [[statistic|0Q5CG2NEQ8]] on each of the $n$ samples obtained by leaving out one observation.\nFrom these leave-one-out values it estimates the [[bias|0BWGTN92FG]] and the variance of the statistic and gives a bias-corrected estimate.\nQuenouille introduced it for bias reduction, and Tukey proposed its use for variance estimation.\nIt works well for smooth statistics, and it is a forerunner of the [[bootstrap|40ATJEVR32]].\n\n## Explanations\n\n### L1 human\n\nTake your sample, remove the first observation, and compute your estimate again.\nPut it back, remove the second one instead, and repeat until each observation has been left out once.\nIf these $n$ estimates move a lot, your estimate is sensitive to individual data points and thus uncertain.\nTheir systematic shift from the full-sample estimate also tells you roughly how biased the estimate is.\n",
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