Prove that Standard Deviation is always $\geq$ Mean Absolute Deviation

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Where $$s = \sqrt{ \frac{1}{n} \sum_{i=1}^{n} (x_i - \bar{x})^2}$$ and $$ M = \frac{1}{n} \sum_{i=1}^{n} |x_i - \bar{x}|$$

I came up with a sketchy proof for the case of $2$ values, but I would like a way to generalize (my "proof" unfortunately doesn't, as far as I can tell).

  • Proof for $2$ values (I would appreciate feedback on this as well):

$$\frac{1}{\sqrt{2}} \sqrt{(x_1 - \bar{x})^2 + (x_2 - \bar{x})^2} \geq \frac{1}{2} (|x_1- \bar{x}| + |x_2- \bar{x}|)$$

Now let $|x_1- \bar{x}| = a$ and $|x_2- \bar{x}| = b$ be the $2$ legs of a right triangle and $\sqrt{(x_1 - \bar{x})^2 + (x_2 - \bar{x})^2} = c$ its hypothenuse.

And let $\theta$ be the angle between $c$ and either $a$ or $b$.

Then $(\sin{\theta} + \cos{\theta}) = \frac{a}{c} + \frac{b}{c} = \frac{a+b}{c} = \frac{\frac{1}{2} (|x_1- \bar{x}| + |x_2- \bar{x}|)}{\frac{1}{\sqrt{2}} \sqrt{(x_1 - \bar{x})^2 + (x_2 - \bar{x})^2}}$

so that $$ \sqrt{2} \geq (\sin{\theta} + \cos{\theta})$$

And we know that $\max(\sin{\theta} + \cos{\theta}) = \sqrt{2}$. QED?

I have no idea how to prove the general case, though.

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Let $y_i = x_i - \bar{x}$ then by Cauchy-Schwarz we have the following:

$\sum_{i=1}^n |y_i| \frac{1}{n} \leq \|y\|_2 \sqrt{\sum_{i=1}^n \frac{1}{n^2}} = \sqrt{\frac{1}{n}\sum_{i=1}^n |x_i - \bar{x}|^2}$