In statistics, pooled variance (also known as combined variance, composite variance, or overall variance, and written ) is a method for estimating variance of several different populations when the mean of each population may be different, but one may assume that the variance of each population is the same. The numerical estimate resulting from the use of this method is also called the pooled variance. Under the assumption of equal population variances, the pooled sample variance provides a higher precision estimate of variance than the individual sample variances. This higher precision can lead to increased statistical power when used in statistical tests that compare the populations, such as the t-test. The square root of a pooled variance estimator is known as a pooled standard deviation (also known as combined standard deviation, composite standard deviation, or overall standard deviation). In statistics, many times, data are collected for a dependent variable, y, over a range of values for the independent variable, x. For example, the observation of fuel consumption might be studied as a function of engine speed while the engine load is held constant. If, in order to achieve a small variance in y, numerous repeated tests are required at each value of x, the expense of testing may become prohibitive. Reasonable estimates of variance can be determined by using the principle of pooled variance after repeating each test at a particular x only a few times. The pooled variance is an estimate of the fixed common variance underlying various populations that have different means. We are given a set of sample variances , where the populations are indexed ,

Assuming uniform sample sizes, , then the pooled variance can be computed by the arithmetic mean: If the sample sizes are non-uniform, then the pooled variance can be computed by the weighted average, using as weights the respective degrees of freedom (see also: Bessel's correction): The unbiased least squares estimate of (as presented above), and the biased maximum likelihood estimate below: are used in different contexts.