= Estimation error
{title2=$\widehat\theta-\theta$}
An <estimation error> is the difference between an <estimator> and the <statistical parameter> it estimates. It is a <random variable> before observing the sample. Its <mean> is the <bias> of the <estimator>, and its mean square is the sum of the <variance> and squared <bias>. Standardizing an <estimation error> by its <standard error> often yields an approximate <standard normal distribution>, allowing construction of a <Wald confidence interval>.
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