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. Now, if the null hypothesis actually was true, you would be committing a type one error without knowing about it. But for power, we are concerned with a type two error. So in this one, it's a conditional probability that our null hypothesis is false.