So if we assume the null hypothesis is true, what would be our sampling distribution? Remember, what we do in significance tests is we have some form of a population, let me draw that. You have a population right over here, and our hypotheses are making some statement about a parameter in that population, and to test it, we take a sample of a certain size, we calculate a statistic, in this case, we would be the sample mean, and we say if we assume that our null hypothesis is true, what is the probability of getting that sample statistic? And if that's below a threshold, which we call a significance level, we reject the null hypothesis. And so that world that we have been living in, one way to think about it, in a world where you assume the null hypothesis is true, you might have a sampling distribution that looks something like this. The null hypothesis is true, then the center of your sampling distribution would be right over here at mu one, and given your sample size, you would get a certain sampling distribution for the sample means. If your sample size increases, this will be narrow, or if it decreases, this thing is going to be wider, and you set a significance level, which is essentially your probability of rejecting the null hypothesis, even if it is true.