This is what we always do in our hypothesis testing. We're going to assume our null hypothesis, and then essentially figure out what are the chances of getting a certain statistic this extreme. And I haven't even defined what that statistic are. So we're going to define, we're going to assume our null hypothesis, and then we're going to come up with a statistic called the F statistic. So our F statistic, which has an F distribution, and we won't go in real deep into the details of the F distribution, but you can already start to think of it as a ratio of 2 chi-squared distributions that may or may not have different degrees of freedom. Our F statistic is going to be the ratio of our sum of squares between the samples. So it's going to be our sum of squares between, divided by our degrees of freedom between, and sometimes this is called the mean squares between MSB, either way, it's going to be that, divided by the sum of squares within.