= Standard score
{title2=$z=(x-\mu)/\sigma$}
{wiki}
= Z-score
{c}
{synonym}
A <standard score> expresses an outcome $x$ as a signed number of <standard deviations> from a <mean> $\mu$, assuming $0<\sigma<\infty$. It is a <dimensionless quantity>. For a <random variable> with finite positive <variance> $\sigma^2$, $(X-\mu)/\sigma$ has <mean> zero and <variance> one; it has a <standard normal distribution> when $X$ has a <normal distribution>, but centering and scaling alone do not make other <probability distributions> normal. https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2022/mit18_05_s22_class06-prep-b.pdf[MIT 18.05, Reading 6b, §5.1: standardization]{ref}.
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