Empirical variance ($T_n=n^{-1}{\sum }_{i=1}^n(X_i-{\overline{X}}_n{)}^2$)

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.