A commonly used measure of dispersion is the **standard deviation**, which
is simply the square root of the **variance**. The variance of a data set is
calculated by taking the arithmetic mean of the squared differences between each
value and the mean value. Squaring the difference has at least three advantages:

Squaring makes each term positive so that values above the mean do not
cancel values below the mean.

Squaring adds more weighting to the larger differences, and in many cases
this extra weighting is appropriate since points further from the mean may be
more significant.

The mathematics are relatively manageable when using this measure in
subsequent statistical calculations.

Because the differences are squared, the units of variance are not the same
as the units of the data. Therefore, the standard deviation is reported as the
square root of the variance and the units then correspond to those of the data
set.

The calculation and notation of the variance and standard deviation depends
on whether we are considering the entire population or a sample set. Following
the general convention of using Greek characters to express population
parameters and Arabic characters to express sample statistics, the notation for
standard deviation and variance is as follows:

for a **
population**
or for the **
sample**

The variance of a sampled subset of observations is calculated in a similar
manner, using the appropriate notation for sample mean and number of
observations. However, while the sample mean is an unbiased estimator of the
population mean, the same is not true for the sample variance if it is
calculated in the same manner as the population variance. If one took all
possible samples of *n* members and calculated the sample variance of each
combination using *n* in the denominator and averaged the results, the
value would not be equal to the true value of the population variance; that is,
it would be biased. This bias can be corrected by using *( n - 1 )* in the
denominator instead of just *n*, in which case the sample variance becomes
an unbiased estimator of the population variance.

}
*
Variance* =
(**SD**)^{2}

Standard deviation and variance are commonly used measures of dispersion.
Additional measures include the *range* and *average deviation*.