Absolute deviation

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"Mean deviation" redirects here. For the music history book, see Mean Deviation (book).

In statistics, the absolute deviation of an element of a data set is the absolute difference between that element and a given point. Typically the deviation is reckoned from the central value, being construed as some type of average, most often the median or sometimes the mean of the data set.

D_i = |x_i-m(X)|


Di is the absolute deviation,
xi is the data element
and m(X) is the chosen measure of central tendency of the data set—sometimes the mean (\overline{x}), but most often the median.

Measures of dispersion[edit]

Several measures of statistical dispersion are defined in terms of the absolute deviation.

Average absolute deviation (general form)[edit]

The average absolute deviation, or simply average deviation of a data set is the average of the absolute deviations from a central point and is a summary statistic of statistical dispersion or variability. In this general form, the central point can be the mean, median, mode, or the result of another measure of central tendency. See below for distinctions where average is synonymous with "mean" and the central point is also the "mean".

The average absolute deviation of a set {x1, x2, ..., xn} is

\frac{1}{n}\sum_{i=1}^n |x_i-m(X)|.

The choice of measure of central tendency, m(X), has a marked effect on the value of the average deviation. For example, for the data set {2, 2, 3, 4, 14}:

Measure of central tendency m(X) Average absolute deviation
Mean = 5 \frac{|2 - 5| + |2 - 5| + |3 - 5| + |4 - 5| + |14 - 5|}{5} = 3.6
Median = 3 \frac{|2 - 3| + |2 - 3| + |3 - 3| + |4 - 3| + |14 - 3|}{5} = 2.8
Mode = 2 \frac{|2 - 2| + |2 - 2| + |3 - 2| + |4 - 2| + |14 - 2|}{5} = 3.0

The average absolute deviation from the median is less than or equal to the average absolute deviation from the mean. In fact, the average absolute deviation from the median is always less than or equal to the average absolute deviation from any other fixed number.

The average absolute deviation from the mean is less than or equal to the standard deviation; one way of proving this relies on Jensen's inequality.

For the normal distribution, the ratio of mean absolute deviation to standard deviation is \scriptstyle \sqrt{2/\pi} = 0.79788456\dots. Thus if X is a normally distributed random variable with expected value 0 then, see Geary (1935):[1]

 w=\frac{ E|X| }{ \sqrt{E(X^2)} } = \sqrt{\frac{2}{\pi}}.

In other words, for a normal distribution, mean absolute deviation is about 0.8 times the standard deviation. However in-sample measurements deliver values of the ratio of mean average deviation / standard deviation for a given Gaussian sample n with the following bounds:  w_n \in [0,1] , with a bias for small n.[2]

Mean absolute deviation (MAD) (about mean)[edit]

The mean absolute deviation (MAD), also referred to as the "mean deviation" or sometimes "average absolute deviation" is the mean of the data's absolute deviations about the data's mean: the average (absolute) distance from the mean. "Average absolute deviation" can refer to either this usage, or to the general form with respect to a specified central point (see above).

MAD has been proposed to be used in place of standard deviation since it corresponds better to real life.[3] Because the MAD is a simpler measure of variability than the standard deviation, it can be used as pedagogical tool to help motivate the standard deviation.[4][5]

This method's forecast accuracy is very closely related to the mean squared error (MSE) method which is just the average squared error of the forecasts. Although these methods are very closely related, MAD is more commonly used because it is both easier to compute (avoiding the need for squaring)[6] and easier to understand.[7]

More recently, the mean absolute deviation about mean is expressed as a covariance between a random variable and its under/over indicator functions;[8]

D_m = E|X-\mu|=2Cov(X,I_O)


Dm is the expected value of the absolute deviation about mean,
"Cov" is the covariance between the random variable X and the over indicator function (I_{O}).

and the over indicator function is defined as

\mathbf{I}_O :=
1 &\text{if } x >\mu, \\
0 &\text{else }

Based on this representation new correlation coefficients are derived. These correlation coefficients ensure high stability of statistical inference when we deal with distributions that are not symmetric and for which the normal distribution is not an appropriate approximation. Moreover an easy and simple way for a semi decomposition of Pietra’s index of inequality is obtained.

Average absolute deviation about median[edit]

Mean absolute deviation about median (MAD median) offers a direct measure of the scale of a random variable about its median

D_{med} = E|X-median|

For the normal distribution we have D_{med} = \sigma \sqrt(2/\pi) . Since the median minimizes the average absolute distance, we have D_{med} <= D_{mean} . By using the general dispersion function Habib (2011) defined MAD about median as

D_{med} = E|X-median|=2Cov(X,I_O)

where the indicator function is

\mathbf{I}_O :=
1 &\text{if } x > median, \\
0 &\text{else }

This representation allows for obtaining MAD median correlation coefficients;[9]

Median absolute deviation (MAD) (about median)[edit]

The median absolute deviation (also MAD) is the median of the absolute deviation from the median. It is a robust estimator of dispersion.

For the example {2, 2, 3, 4, 14}: 3 is the median, so the absolute deviations from the median are {1, 1, 0, 1, 11} (reordered as {0, 1, 1, 1, 11}) with a median of 1, in this case unaffected by the value of the outlier 14, so the median absolute deviation (also called MAD) is 1.

Maximum absolute deviation[edit]

The maximum absolute deviation about a point is the maximum of the absolute deviations of a sample from that point. While not strictly a measure of central tendency, the maximum absolute deviation can be found using the formula for the average absolute deviation as above with m(X)=\text{max}(X), where \text{max}(X) is the sample maximum. The maximum absolute deviation cannot be less than half the range.


The measures of statistical dispersion derived from absolute deviation characterize various measures of central tendency as minimizing dispersion: The median is the measure of central tendency most associated with the absolute deviation. Some location parameters can be compared as follows:

  • L2 norm statistics: the mean minimizes the mean squared error
  • L1 norm statistics: the median minimizes average absolute deviation,
  • L norm statistics: the mid-range minimizes the maximum absolute deviation
  • trimmed L norm statistics: for example, the midhinge (average of first and third quartiles) which minimizes the median absolute deviation of the whole distribution, also minimizes the maximum absolute deviation of the distribution after the top and bottom 25% have been trimmed off.


The mean absolute deviation of a sample is a biased estimator of the mean absolute deviation of the population. In order for the absolute deviation to be an unbiased estimator, the expected value (average) of all the sample absolute deviations must equal the population absolute deviation. However, it does not. For the population 1,2,3 both the population absolute deviation about the median and the population absolute deviation about the mean are 2/3. The average of all the sample absolute deviations about the mean of size 3 that can be drawn from the population is 44/81, while the average of all the sample absolute deviations about the median is 4/9. Therefore the absolute deviation is a biased estimator.
However, this argument is based on the notion of mean-unbiasedness. Each measure of location has its own form of unbiasedness (see entry on biased estimator. The relevant form of unbiasedness here is median unbiasedness.

See also[edit]


  1. ^ Geary, R. C. (1935). The ratio of the mean deviation to the standard deviation as a test of normality. Biometrika, 27(3/4), 310-332.
  2. ^ See also Geary's 1936 and 1946 papers: Geary, R. C. (1936). Moments of the ratio of the mean deviation to the standard deviation for normal samples. Biometrika, 28(3/4), 295-307 and Geary, R. C. (1947). Testing for normality. Biometrika, 34(3/4), 209-242.
  3. ^ http://www.edge.org/response-detail/25401
  4. ^ Kader, Gary (March 1999). "Means and MADS". Mathematics Teaching in the Middle School 4 (6): 398–403. Retrieved 20 February 2013. 
  5. ^ Franklin, Christine, Gary Kader, Denise Mewborn, Jerry Moreno, Roxy Peck, Mike Perry, and Richard Scheaffer (2007). Guidelines for Assessment and Instruction in Statistics Education (PDF). American Statistical Association. ISBN 978-0-9791747-1-1. 
  6. ^ Nahmias, Steven; Olsen, Tava Lennon (2015), Production and Operations Analysis (7th ed.), Waveland Press, p. 62, ISBN 9781478628248, MAD is often the preferred method of measuring the forecast error because it does not require squaring. 
  7. ^ Stadtler, Hartmut; Kilger, Christoph; Meyr, Herbert, eds. (2014), Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies, Springer Texts in Business and Economics (5th ed.), Springer, p. 143, ISBN 9783642553097, the meaning of the MAD is easier to interpret .
  8. ^ Elamir, Elsayed A.H. (2012). "On uses of mean absolute deviation: decomposition, skewness and correlation coefficients". Metron: International Journal of Statistics LXX (2-3). 
  9. ^ Habib, Elsayed A.E. (2011). "Correlation coefficients based on mean absolute deviation about median". International Journal of Statistics and Systems 6 (4): pp. 413–428. 

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