In which step of variance calculation do you square the deviations?

The step where you square the deviations in variance calculation is crucial for the process of measuring how much individual data points deviate from the mean. Specifically, after calculating the mean of the dataset, the next logical step is to find the deviations of each data point from the mean. Once those deviations are determined, squaring them comes next. The squaring serves two important purposes: it ensures that all deviations are treated as positive values (which means that negative deviations do not cancel out positive ones), and it accentuates larger deviations, making them more significant in the overall variance.

This squaring step is part of the process that leads to both the population variance and sample variance formulas. After squaring the deviations, you would then sum them up and divide by the appropriate number (either the total number of data points for population variance or one less than that for sample variance) to find the average of those squared deviations. Therefore, the squaring of the deviations is a fundamental part of the process to quantify the variance of a dataset.