{
  "aliases": [
    "law-of-rare-events",
    "poisson-approximation-to-the-binomial"
  ],
  "backlinks": null,
  "body": "\n## Statement\n\nLet $\\lambda \u003e 0$, and let $p_n \\in [0, 1]$ satisfy $n p_n \\to \\lambda$ as $n \\to \\infty$. Let $S_n \\sim \\operatorname{Bin}(n, p_n)$.\nThen for each integer $k \\ge 0$,\n$$\\lim_{n \\to \\infty} P(S_n = k) = \\lim_{n \\to \\infty} \\binom{n}{k} p_n^k (1 - p_n)^{n - k} = e^{-\\lambda} \\frac{\\lambda^k}{k!}.$$\nSo $S_n$ converges [[in distribution|0JDAQHWA9N]] to the [[Poisson distribution|0VEFSSKX18]] with parameter $\\lambda$.\n\n## Remarks\n\nProof: write $\\binom{n}{k} p_n^k = \\frac{n(n-1)\\cdots(n-k+1)}{n^k} \\cdot \\frac{(n p_n)^k}{k!}$. The first factor tends to $1$, and $(1 - p_n)^{n-k} \\to e^{-\\lambda}$.\nIn practice, $\\operatorname{Bin}(n, p)$ is close to $\\operatorname{Poisson}(np)$ when $n$ is large and $p$ is small.\nLe Cam's inequality gives an explicit bound on the error of this approximation.\n\n## Explanations\n\n### L1 human\n\nThe number of rare events among many independent chances is approximately Poisson.\nExamples are the number of misprints on a page, or the number of large insurance claims in a year.\n",
  "commit": "6ac761d40018146541484a9e8b9eafd6ecc303dc",
  "front": {
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  "msc": [
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  "name": "Poisson limit theorem",
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