To account for this, we increase the value computed for v slightly by \(\frac{n}{n-1}\) to give the sample variance via
\begin{equation*}
s^2 = \frac{n}{n-1} v = \frac{n}{n-1}\frac{\sum_{k=1}^n ( x_k-\overline{x} )^2}{n} = \frac{\sum_{k=1}^n ( x_k-\overline{x} )^2}{n-1}.
\end{equation*}
and the sample standard deviation \(s\) similarly as the square root of the sample variance.