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  "body": "\n## Summary\n\nThe empirical distribution function of a sample is the [[cumulative distribution function|0H8H7ZTHW2]] of the distribution that puts mass $1/n$ on each observation.\nAt each point $t$ it gives the fraction of observations that are at most $t$.\nIt estimates the true distribution function without any parametric assumption, and for an i.i.d. sample the [[Glivenko–Cantelli theorem|E7B78X780X]] shows that it converges to it uniformly, almost surely.\nGoodness-of-fit tests such as the [[Kolmogorov–Smirnov test|YZYHWC6C5F]] compare it with a hypothesised distribution function.\n\n## Explanations\n\n### L1 human\n\nSort your data and draw a staircase that starts at height $0$ and jumps up by $1/n$ at each data point.\nThis staircase is the empirical distribution function.\nFor the data $1, 2, 2, 5$, it is $0$ before $1$, $1/4$ on $[1, 2)$, $3/4$ on $[2, 5)$ and $1$ from $5$ on.\nWith more and more data, the staircase hugs the true distribution function more and more closely.\n",
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