Our estimate of the true average \( \mu_{X} \) is the sample mean \( \langle X_m \rangle \) $$ \begin{equation*} \mu_{X}^{\phantom X} \approx X_m=\frac{1}{mn}\sum_{\alpha=1}^m\sum_{k=1}^n x_{\alpha,k}. \end{equation*} $$

We can then use Eq. (9) $$ \begin{equation*} \sigma^2_m=\frac{1}{mn^2}\sum_{\alpha=1}^m\sum_{kl=1}^n (x_{\alpha,k}-\langle X_m \rangle)(x_{\alpha,l}-\langle X_m \rangle), \end{equation*} $$ and rewrite it as $$ \begin{equation*} \sigma^2_m=\frac{\sigma^2}{n}+\frac{2}{mn^2}\sum_{\alpha=1}^m\sum_{k < l}^n (x_{\alpha,k}-\langle X_m \rangle)(x_{\alpha,l}-\langle X_m \rangle), \end{equation*} $$ where the first term is the sample variance of all \( mn \) experiments divided by \( n \) and the last term is nothing but the covariance which arises when \( k\ne l \).