Similar to multiple two-sample t-tests, but with less type i error. If you set the type one error to be . 05, and you had several groups, each time you tested a mean against another there would be a . 05 probability of having a type one error rate. This would mean that with six t-tests you would have a 0. 30 (. 05 6) probability of having a type one error rate. This is much higher than the desired . 05. Anova creates a way to test several null hypothesis at the same time. The logic behind this procedure has to do with how much variance there is in the population. It is likely the researcher will not know the actual variance in the population but they can estimate this by sampling and calculating the variance in the sample. You compare the differences in the samples to see if they are the same or statistically different while still accounting for sampling error.