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. You could even view it as, and we've talked about it, you can view your significance level as a probability of making a type one error. So your significance level is some area, and so let's say it's this area that I'm shading in orange right over here, that would be your significance level. So if you took a sample right over here, and you calculated its sample mean, and you happen to fall in this area, or this area, or this area right over here, then you would reject your null hypothesis.