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  "body": "\n## Definition\n\nFor each $n \\ge 1$, let $\\hat{\\theta}_n$ be an [[estimator|0TTNRP5PFQ]] of a real parameter $\\theta$ that uses the first $n$ observations of a sample.\nThe sequence $(\\hat{\\theta}_n)$ is consistent if, for each $\\theta \\in \\Theta$, $\\hat{\\theta}_n \\to \\theta$ [[in probability|0EFDDXTVD6]] when $\\theta$ is the true parameter:\n$$\\lim_{n \\to \\infty} P_\\theta\\left(|\\hat{\\theta}_n - \\theta| \u003e \\varepsilon\\right) = 0 \\quad \\text{for each } \\varepsilon \u003e 0.$$\n\n## Remarks\n\nIf the convergence is almost sure, the sequence is strongly consistent.\nBy the weak law of large numbers, the sample mean of an i.i.d. sample is a consistent estimator of the mean.\nIf the mean squared error of $\\hat{\\theta}_n$ tends to $0$, then $(\\hat{\\theta}_n)$ is consistent. This follows from Markov's inequality.\nConsistent estimators can be biased, and unbiased estimators can fail to be consistent. For example, the estimator $X_1$ of the mean uses only one observation: it is unbiased but not consistent, unless $X_1$ is almost surely constant.\n\n## Explanations\n\n### L1 human\n\nA consistent estimator gets closer and closer to the truth when you collect more and more data.\nThis is a minimal requirement for a reasonable estimator.\n",
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