In statistics, the<unk>  of an element of a data set is the<unk>  difference between that element and a given point.  Typically the point from which the<unk>  is measured is the value of either the median or the mean of the data set.
where
The average<unk>  (or simply average<unk>  of a data set is the average (or expected value) of the<unk> s and is a summary statistic of statistical dispersion or variability.
The average<unk>  of a set {x1, x2, ..., xn} is
The choice of measure of Central tendency has a marked effect on the value of the average<unk> .  For example, for the data set {2, 2, 3, 4, 14}:
The average<unk>  from the median is less than or equal to the average<unk>  from the mean.  In fact, the average<unk>  from the median is always less than or equal to the average<unk>  from any other fixed number.
The average<unk>  from the mean is less than or equal to the standard<unk> .  One way of proving that relies on Jensen's inequality.
The mean<unk>  is the average<unk>  from the mean and is a common measure of forecast error in time series analysis.  It should be noted that although the term mean<unk>  is used as a synonym for mean<unk> , to be precise it is not the same; in its strict interpretation (namely, omitting the<unk>  value operation), the mean<unk>  of any data set from its mean is always zero.
The mean<unk>  of a sample is a biased estimator of the mean<unk>  of the population.
Absolute deviation
Ancillary statistic
Degrees of freedom 
Attributable risk
Quantum yield
Statistical significance
Random variable
Hazard ratio