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In statistics and optimization, statistical errors and residuals are easily confused measures of deviation of a sample of its "theoretical value". The error of a sample is the sample variance of the (unobservable) value and the true function, while the rest of a sample is the difference between the sample and the value of the estimated function. The distinction is important in regression analysis where it leads to the notion of studentized residuals.

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