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Edwin Bidwell Wilson was a mathematician and statistician, remembered in statistics for the Wilson score interval. His 1931 paper with Margaret M. Hilferty introduced an influential approximation to the chi-squared distribution. His firsthand National Academy of Sciences memoir lists the paper under his full name.^[ref]

Their Wilson-Hilferty transformation uses $(X/k{)}^{1/3}$ for a chi-squared distribution with $k$ statistical degrees of freedom. Its approximate mean $1-2/(9k)$ and variance $2/(9k)$ give a normal approximation useful for converting quantiles between the two distributions.^[ref]

By Edwin Bidwell Wilson, founded upon the lectures of Josiah Willard Gibbs. The title page names Wilson as author; Gibbs' lectures provide the stated foundation.

Charles Scribner's Sons, 1901.

Digitized volume: archive.org/details/vectoranalysisa00wilsgoog

For $K\sim \mathrm{Bin}(n,p)$, $n>0$, put $\hat{p}=K/n$ and let $z$ be the $1-\alpha /2$ quantile of the standard normal distribution. Inverting the approximate score test retains precisely the parameters satisfyingMultiplication and collection of terms gives the quadratic inequalityIts two roots are the displayed endpoints. The Wilson score interval stays within $[0,1]$ and remains nontrivial when $K=0$ or $K=n$, unlike the unmodified Wald confidence interval. Its coverage probability is approximate and varies with $p$; it is not a uniformly exact 95% guarantee. Separate pointwise intervals for several groups are not a simultaneous confidence statement. The method is named after Edwin Bidwell Wilson.^[ref]