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Variance is a statistical measure that quantifies the dispersion or spread of a set of data points around their mean. It represents the average of the squared differences from the mean. A low variance indicates that the data points are clustered closely around the mean, while a high variance suggests that the data points are spread out over a wider range.

The formula for sample variance is typically denoted by s² and calculated as the sum of the squared differences between each data point and the sample mean, divided by one less than the number of data points (n-1). This division by n-1, known as Bessel's correction, provides a less biased estimate of the population variance when working with a sample. The formula for population variance, denoted by σ², uses the number of data points (N) in the denominator instead of N-1.

Variance is a fundamental concept in inferential statistics and is used in various statistical tests and models, such as ANOVA (Analysis of Variance) and regression analysis. It is always a non-negative value. While variance itself is useful, its square root, the standard deviation, is often preferred for interpretation because it is in the same units as the original data.