Which of the following accurately describes the population variance?

The population variance is defined as the average of the squared deviations from the mean, making this description accurate. To compute the population variance, you first calculate the mean (average) of the data set. Next, you determine the deviations of each data point from the mean, square each of these deviations to eliminate negative values, and finally, average those squared deviations. This provides a measure of how spread out the data points are in relation to the mean, which is central to understanding variability within a population.

The other options do not accurately describe the population variance. The assertion that it is always equal to the sample variance is misleading since the population variance refers to the entire population's data, whereas sample variance is calculated from a subset of that population. Stating that it is the square root of the standard deviation reflects a misconception, as standard deviation is the square root of the variance, not the other way around. Lastly, variance, whether from a population or sample, cannot be negative, as it is derived from squared values, which are always non-negative. This further highlights the correctness of the first choice as the only accurate description of population variance.