In statistics, McKay's approximation of the coefficient of variation is a statistic based on a sample from a normally distributed population. It was introduced in 1932 by A. T. McKay.^[1] Statistical methods for the coefficient of variation often utilizes McKay's approximation.^[2][3][4][5]

Let ${\displaystyle x_{i}}$, ${\displaystyle i=1,2,\ldots ,n}$ be ${\displaystyle n}$ independent observations from a ${\displaystyle N(\mu ,\sigma ^{2})}$ normal distribution. The population coefficient of variation is ${\displaystyle c_{v}=\sigma /\mu }$. Let ${\displaystyle {\bar {x}}}$ and ${\displaystyle s\,}$ denote the sample mean and the sample standard deviation, respectively. Then ${\displaystyle {\hat {c}}_{v}=s/{\bar {x}}}$ is the sample coefficient of variation. McKay's approximation is

${\displaystyle K=\left(1+{\frac {1}{c_{v}^{2}}}\right)\ {\frac {(n-1)\ {\hat {c}}_{v}^{2}}{1+(n-1)\ {\hat {c}}_{v}^{2}/n}}}$

Note that in this expression, the first factor includes the population coefficient of variation, which is usually unknown. When ${\displaystyle c_{v}}$ is smaller than 1/3, then ${\displaystyle K}$ is approximately chi-square distributed with ${\displaystyle n-1}$ degrees of freedom. In the original article by McKay, the expression for ${\displaystyle K}$ looks slightly different, since McKay defined ${\displaystyle \sigma ^{2}}$ with denominator ${\displaystyle n}$ instead of ${\displaystyle n-1}$. McKay's approximation, ${\displaystyle K}$, for the coefficient of variation is approximately chi-square distributed, but exactly noncentral beta distributed .^[6]