# Precision (statistics)

In statistics, the precision matrix or concentration matrix is the matrix inverse of the covariance matrix or dispersion matrix, ${\displaystyle P=\Sigma ^{-1}}$.[1][2][3] For univariate distributions, the precision matrix degenerates into a scalar precision, defined as the reciprocal of the variance, ${\displaystyle p={\frac {1}{\sigma ^{2}}}}$.[4]
Other summary statistics of statistical dispersion also called precision (or imprecision[5][6]) include the reciprocal of the standard deviation, ${\displaystyle p={\frac {1}{\sigma }}}$;[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]