= Central moment
{title2=$\mathbb E[(X-\mathbb EX)^n]$}
{wiki}
For a <random variable> with finite mean $\mu$, its $n$th <central moment> is $\mathbb E[(X-\mu)^n]$ when defined. The first is zero; the second is the <variance>; the standardized third gives <skewness>. Translating $X$ by a constant leaves central moments unchanged, because the mean translates by the same amount. For equally likely values one and three, the second central moment is one, whereas the second <raw moment> is five. Odd central moments can cancel between positive and negative deviations, so an <absolute moment> provides a different integrability check.
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