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  "body": "\n## Summary\n\nFrequentist inference is the school of [[statistical inference|JZ2S01BHJJ]] in which the unknown parameter is a fixed quantity, not a random one.\nA procedure is judged by how it behaves over hypothetical repetitions of the experiment.\nThat is, it is judged by its [[sampling distribution|656Y8FA3RY]] under each value of the parameter.\nConfidence intervals, p-values, significance tests and unbiased estimators are its main tools.\nIt contrasts with [[Bayesian inference|0JWRJX2RMP]], which puts a probability distribution on the parameter.\n\n## Explanations\n\n### L1 human\n\nA frequentist asks: if I used this method again and again on fresh data, how often would it give the right answer?\nA $95\\%$ confidence interval is a method that captures the true value in $95\\%$ of repetitions, whatever that true value is.\nIt does not say that the true value lies in today's interval with probability $95\\%$; the true value is fixed, and today's interval either contains it or not.\n",
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