Chebyshev's inequality links the distribution function to this quantity for arbitrary distributions, and the stronger "empirical rule" can be used for normally distributed data.
An approximate unbiased estimator for this quantity is given by a "one over n minus 1.5" estimator. It is invariant to change of location and scales directly with change of scale. Chebyshev's inequality links the distribution function to this quantity for arbitrary distributions, and the stronger "empirical rule" can be used for normally distributed data. Unlike a related measure of dispersion, it takes the same units as the data it describes. For ten points, identify this quantity that for discrete distributions is the root-mean-square distance of values from the mean, the square root of the variance.
An approximate unbiased estimator for this quantity is given by a "one over n minus 1.5" estimator.
{Standard Deviation}