Many studies, including most controlled clinical trials, contrast data from two different groups of subjects. Observations which are measurements are often analysed by the t test, a method which assumes that the data in the different groups come from populations where the observations have a normal distribution and the same variances (or standard deviations). While the t test is well known, many researchers seem unaware of the correct method for comparing three or more groups. For example, table 1 shows measurements of galactose binding for three groups of patients. A common error is to compare each pair of groups using separate two sample t tests1 with the consequent problem of multiple testing.2 The correct approach is to use one way analysis of variance (also called ANOVA), which is based on the same assumptions as the t test. We compare the groups to evaluate whether there is evidence that the means of the populations differ. Why then is the method called analysis of variance?