If data comes from a sample of the population then we call it a sample variance and denote this value by
\begin{equation*}
v = \frac{\sum_{k=1}^n ( x_k-\overline{x} )^2}{n}.
\end{equation*}
Sample data tends to reflect certain "biases". For example, a small data set is unlikely to contain a member of the data set that is far away from the major portion of the data. However, data values that are far from the mean provide a much greater contribution to the calculation of v than do values that are close to the mean. Technically, bias is defined mathematically using something called "expected value" and would be discussed in a course that might follow this one.