Which term describes the average squared deviation from the mean?

The term that describes the average squared deviation from the mean is variance. Variance is a statistical measure that quantifies the degree of spread or dispersion in a set of data points. Specifically, it calculates the average of the squared differences between each data point and the mean of the dataset. By squaring the deviations, variance ensures that the differences are all positive and emphasizes larger deviations from the mean. This property makes variance a useful tool for understanding the variability within a dataset, particularly in relation to its mean.

On the other hand, standard deviation, while related, is not the same as variance. Standard deviation is the square root of variance and provides a measure of dispersion that is in the same units as the original data, making it more interpretable in certain contexts. The other options, such as sample and mode, refer to different concepts in statistics and do not pertain specifically to the average squared deviations from the mean. A sample refers to a subset of a population used for statistical analysis, while mode identifies the most frequently occurring value in a dataset. Therefore, variance is the correct term for the average squared deviation from the mean.