The variance of a dataset is

Variance is a measure of how spread out the data points are from their average value.
Mathematically, it is calculated by taking each data point, subtracting the mean to find the difference, squaring that difference (to ensure all values are positive and to emphasize larger deviations), summing these squared differences, and then dividing by the total number of data points. This process perfectly matches Option C's description: "The average of the squared differences from the Mean."
While Option B is related because variance is indeed the square of the standard deviation, it describes a relationship rather than the fundamental definition.
Option A is incorrect because variance involves squaring differences, not taking a square root of the mean (which would relate to standard deviation).
Option D is incorrect because it describes the arithmetic mean itself, not a measure of dispersion.
Therefore, understanding that variance quantifies spread through averaged squared deviations confirms Option C as the correct choice.