Precision (statistics) in the context of "Likelihood"

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

Other summary statistics of statistical dispersion also called precision (or imprecision)include the reciprocal of the standard deviation, ${\displaystyle p={\frac {1}{\sigma }}}$; the standard deviation itself and the relative standard deviation;as well as the standard error and the confidence interval (or its half-width, the margin of error).