One, if we assume that the null hypothesis is true, and one where we assume that the null hypothesis is false and the true population parameter is something different than the null hypothesis is saying. So for example, let's say that we have a null hypothesis that our population mean is equal to, let's just call it mu one, and we have an alternative hypothesis, so H sub A, that says, hey, no, the population mean is not equal to mu one. So if you assumed a world where the null hypothesis is true, so I'll do that in blue. 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.