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  "body": "\n## Summary\n\nPoint estimation is the part of statistical inference that computes from data a single best guess of an unknown quantity.\nA typical unknown quantity is a parameter of a statistical model.\nThe rule that turns data into the guess is an [[estimator|0SSS6ERQ0S]], and its value on the observed data is the estimate.\nThe theory compares estimators by bias, variance, mean squared error, consistency and efficiency.\nIt also gives general methods to build them, such as maximum likelihood and the method of moments.\nIt is contrasted with interval estimation, which gives a range of plausible values.\n\n## Explanations\n\n### L1 human\n\nYou flip a coin $100$ times and see $58$ heads; your guess for the probability of heads is $0.58$.\nThat single number is a point estimate, and the rule \"number of heads divided by number of flips\" is the estimator.\nA different sample would give a different guess, so an estimator is itself random.\nPoint estimation studies which rules give guesses that are close to the truth most of the time.\n",
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