The mean deviation (also called the mean absolute deviation) is the mean of the absolute deviations of a set of data about the data's mean. For a sample size , the mean deviation is defined by

(1)

where is the mean of the distribution. The mean deviation of a list of numbers is implemented in Mathematica as MeanDeviation[data].
The mean deviation for a discrete distribution defined for , 2, ..., is given by

(2)

Mean deviation is an important descriptive statistic that is not frequently encountered in mathematical statistics. This is essentially because while mean deviation has a natural intuitive definition as the "mean deviation from the mean," the introduction of the absolute value makes analytical calculations using this statistic much more complicated than the standard deviation

(3)

As a result, least squares fitting and other standard statistical techniques rely on minimizing the sum of square residuals instead of the sum of absolute residuals.
For example, consider the discrete uniform distribution consisting of possible outcomes with for , 2, ..., . The mean is given by

(4)

The variance (and therefore its square root, namely the standard deviation) is also straightforward to obtain as

(5)

On the other hand, the mean deviation is given by

(6)

This can be obtained in closed form, but is much more unwieldy since it requires breaking up the summand into two pieces and treating the cases of even and odd separately.
The following table summarizes the mean absolute deviations for some named continuous distributions, where is an incomplete beta function, is a beta function, is a gamma function, is the Euler-Mascheroni constant, is a Meijer G-function, is the exponential integral function, is erf, and is erfc.

__________________