The sample variance 
(commonly written 
or sometimes 
)
is the second sample central moment and is
defined by
 |
(1)
|
where 
the sample mean and 
is the sample size.
To estimate the population variance 
from a sample of 
elements with a priori unknown mean
(i.e., the mean is estimated from the sample itself), we
need an unbiased estimator 
for 
. This estimator is given
by k-statistic 
, which is defined by
 |
(2)
|
(Kenney and Keeping 1951, p. 189). Similarly, if 
samples are taken from a distribution with underlying central
moments 
,
then the expected value of the observed sample variance 
is
 |
(3)
|
Note that some authors (e.g., Zwillinger 1995, p. 603) prefer the definition
 |
(4)
|
since this makes the sample variance an unbiased estimator for the population variance. The distinction between 
and 
is a common source of confusion, and extreme care
should be exercised when consulting the literature to determine which convention
is in use, especially since the uninformative notation 
is commonly used for both. The unbiased sample variance 
is implemented as Variance[list].
Also note that, in general, 
is not an unbiased
estimator of the standard deviation 
even if 
is an unbiased estimator
for 
.
