Exercises · Q15
Q.Why is the Standard Deviation generally preferred over the Mean Deviation as a measure of dispersion in rigorous economic analysis, even though the Mean Deviation is simpler to calculate?
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Start your 14-day free trial to unlock the full solution →Both measures deal with the same basic problem: individual deviations from the mean can be positive or negative, and if simply added up they cancel out to zero, hiding the true spread. Mean Deviation solves this by taking the absolute value of each deviation; Standard Deviation solves it by squaring each deviation instead.
This difference in approach has real consequences:
- Mathematical tractability — the absolute value function has a sharp 'corner' at zero and cannot be differentiated there, which makes the Mean Deviation awkward to use in further algebraic derivations. Squaring, by contrast, is a smooth operation that behaves well under addition, differentiation, and the whole further apparatus of statistical theory (variance, the correlation coefficient itself is built on squared-deviation sums, and so is regression) — which is exactly why Standard Deviation, not Mean Deviation, is the measure that connects to essentially all of the more advanced tools covered later in this chapter.
- Weighting of large deviations — squaring a deviation of, say, 10 gives 100, while squaring a deviation of 2 gives only 4 — so Standard Deviation naturally gives disproportionately more weight to large deviations from the mean. In economic contexts such as measuring income inequality or price volatility, this is usually a desirable property: a few extreme outliers (a handful of very high incomes, or one unusually volatile price swing) are exactly what such measures are meant to be sensitive to. …
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