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  "body": "\n## Definition\n\nConsider a [[hypothesis test|087P03NEND]] of a null hypothesis $H_0 \\colon \\theta \\in \\Theta_0$ with a test statistic $T(X)$, where large values of $T$ are evidence against $H_0$. Let $t$ be the observed value of $T(X)$.\nThe p-value of the observation is\n$$p(t) = \\sup_{\\theta \\in \\Theta_0} P_\\theta\\big(T(X) \\ge t\\big).$$\nIf $H_0$ is simple, $\\Theta_0 = \\{\\theta_0\\}$, then $p(t) = P_{\\theta_0}(T(X) \\ge t)$.\n\n## Remarks\n\nFor a two-sided test with a statistic that is symmetric about $0$ under $H_0$, one uses $P_{\\theta_0}(|T(X)| \\ge |t|)$.\nThe test that rejects $H_0$ when $p \\le \\alpha$ has significance level $\\alpha$.\nIf $H_0$ is simple and $T(X)$ has a continuous law under $H_0$, then the random p-value $p(T(X))$ has the [[uniform distribution|0WGDPYR30Q]] on $[0, 1]$ under $H_0$.\nThe p-value is not the probability that $H_0$ is true.\n\n## Explanations\n\n### L1 human\n\nThe p-value tells how surprising your data would be if the null hypothesis were true.\nExample: a coin shows $14$ heads in $20$ tosses. If the coin is fair, the probability of at least $14$ heads is about $0.058$. This is the one-sided p-value.\n",
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