= Empirical variance
{title2=$T_n=n^{-1}\sum_{i=1}^n(X_i-\overline X_n)^2$}
The <empirical variance> is the <variance> of the <empirical distribution>, which puts <probability> $1/n$ on each observation. It uses divisor $n$, whereas the usual <sample variance> uses divisor $n-1$. For <independent and identically distributed random variables> with finite second <moment>, <jackknife bias correction> converts the <empirical variance> into the <unbiased estimator> $s_n^2$ exactly.
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