# Precision (statistics)

## Reciprocal of the statistical variance / From Wikipedia, the free encyclopedia

For broader coverage of this topic, see Accuracy and precision.

In statistics, the **precision matrix** or **concentration matrix** is the matrix inverse of the covariance matrix or dispersion matrix, $P=\Sigma ^{-1}$.^{[1]}^{[2]}^{[3]}
For univariate distributions, the precision matrix degenerates into a scalar **precision**, defined as the reciprocal of the variance, $p={\frac {1}{\sigma ^{2}}}$.^{[4]}

Other summary statistics of statistical dispersion also called *precision* (or *imprecision*^{[5]}^{[6]})
include the reciprocal of the standard deviation, $p={\frac {1}{\sigma }}$;^{[3]}
the standard deviation itself and the relative standard deviation;^{[7]}
as well as the standard error^{[8]} and the confidence interval (or its half-width, the margin of error).^{[9]}