How is variance calculated in a data set?

Variance measures how much the values in a data set differ from the mean of that set. To calculate variance, the first step is to find the mean, which is the average of the data points. Next, for each individual value in the data set, the difference between that value and the mean is calculated. This step highlights how far each value is from the average.

The crucial part of finding variance involves squaring these differences. Squaring is necessary because it eliminates any negative values and ensures that larger deviations are given more weight. After squaring all the differences, the next step is to average these squared differences. This average is what constitutes the variance of the data set.

By this process, variance provides a quantitative measure of dispersion within the data set, illustrating how spread out the numbers are around the mean.