Statistics Definitions > Within Mean Square

## What is Within Mean Square?

Within Mean Square (WMS) is an estimate of the population variance. It is based on the average of all variances *within* the samples. **Within Mean **is a weighted measure of how much a (squared) individual score varies from the sample mean score. The notation for within mean square error is MS_{within} or you may sometimes see it written as:

Instead of σ the symbol for the population mean, you might also see “s” for the sample mean in the notation.

The formula has several variations, depending on the text. For example, some texts use this (slightly more complicated) formula than the one I’ve outlined in this article:

SS_{within} = sum ( (n_{i}-1)s_{i}^{2} ).

All of the formulas get the same result, so it doesn’t matter which one you choose.

## What is Within Mean Square used for?

Within Mean Square is used to calculate an F ratio in a one way ANOVA. The total sum of squares (SS) is the sum of both the **within mean square** and the **between mean square** (BMS). In a hypothesis test, the ratio BMS/WMS follows the shape of an F Distribution. If the ratio exceeds an F value for the test, it shows that there is a significant difference in your results.

## Between vs. Within

The two terms sound similar, but there is a notable difference between the two terms.

**Between group variation** (sometimes called among group variation) is how much variation there is due to interaction between samples. The mean square between is therefore the mean square among sample means.

**Within group variation **is how much variation there is due to differences within individual samples, so the mean square within is defined as the mean square within samples.

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