Variance measures dispersion by averaging squared deviations from the mean. Squaring is useful mathematically because it avoids negative values and penalises large deviations, but it creates a practical interpretation problem: the result is expressed in squared units. For example, if returns are measured in percentage points, variance is in squared percentage points, which is not intuitive to interpret or compare directly with the original data. Standard deviation is the square root of variance, which converts the dispersion measure back into the same units as the underlying data. This makes standard deviation easier to interpret as a typical distance from the mean and more directly usable in investment contexts, such as comparing volatility across assets, assessing tracking error, and estimating risk budgets. It is not necessarily easier to calculate than variance because it is derived from it, and it is not immune to small sample issues. The examinable reason it is more useful is the unit consistency, which supports clearer communication of risk and more meaningful comparison across portfolios and asset classes.