Population variance is the average of the squared deviation of each individual data point from the population mean for all values of an entire group. The simplest estimators for population mean and population variance are simply the mean and variance of the sample, the sample mean and (uncorrected) sample variance – these are consistent estimators (they converge to the correct value as the number of samples increases), but can be improved. Estimating the population variance by taking the sample’s variance is close to optimal in general, but can be improved in two ways. Most simply, the sample variance is computed as an average of squared deviations about the (sample) mean, by dividing by *n.* However, using values other than *n* improves the estimator in various ways. Four common values for the denominator are *n,* *n* − 1, *n* + 1, and *n* − 1.5: *n* is the simplest (population variance of the sample), *n* − 1 eliminates bias, *n* + 1 minimizes mean squared error for the normal distribution, and *n* − 1.5 mostly eliminates bias in unbiased estimation of standard deviation for the normal distribution.

In general, to calculate this using a *finite* population of size *N* with values *x*_{i} is given by:

#### References

Wikipedia. Variance. https://en.wikipedia.org/wiki/Variance#Population_variance