In statistical terms, what does variance measure?

Variance measures the spread of data points around the mean of a dataset. It quantifies how much the values in a dataset differ from the mean value, providing insights into the degree of dispersion within the dataset. A high variance indicates that the data points are widely spread out from the mean, while a low variance suggests that they are clustered closely around the mean.

This concept is critical in statistics and data analysis, as it helps to understand the distribution and variability of data, which can influence the interpretation of statistical results and the reliability of predictions made from the data.

The other choices pertain to different statistical concepts. The average of a dataset refers to the mean, while frequency of occurrences relates to how often certain values appear in the dataset. The relationship between two datasets would be described by measures of correlation or covariance, not variance. Thus, the definition of variance as the spread of data points around the mean is the most accurate description.