The unadjusted sample variance measures the average dispersion of a sample of observations around their mean.
It is computed by averaging the squared deviations from the mean.
It is also often called biased sample variance, because, under standard assumptions, it is a biased estimator of the population variance.
It is defined as follows:
Definition
Given
observations
,
their unadjusted sample variance
is
where
is their sample
mean:
Suppose that the sample consists of the following
observations:
We arrange the observations into a table and use the table to do all the necessary calculations:
Thus, the sample variance is
Suppose that
are
independent
realizations of
random variables
having the same mean
and the same variance
.
It can be proved (see
Variance
estimation) that the unadjusted sample variance
is a biased estimator of
,
that
is,
where
is the expected
value of
.
The cause of the bias is that we use the sample mean
instead of the true mean
in the calculation.
If
were known and used in the formula in place of
,
then the resulting estimator would be unbiased.
In order to obtain an unbiased estimator of the true variance
,
we need to perform a so-called degrees of freedom
adjustment:
The estimator
is unbiased (see Adjusted sample
variance).
If the observations
are not only independently and identically
distributed, but they are also
normal, then the
biased sample variance coincides with the maximum likelihood estimator of the
variance
(see
Maximum
likelihood estimation of the parameters of a normal distribution).
The lecture entitled Variance estimation provides a thorough introduction to the concept of unadjusted sample variance, including a detailed analysis of its statistical properties (e.g., its bias as an estimator of the population variance).
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Please cite as:
Taboga, Marco (2021). "Unadjusted sample variance", Lectures on probability theory and mathematical statistics. Kindle Direct Publishing. Online appendix. https://www.statlect.com/glossary/unadjusted-sample-variance.
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