So you could say it's the probability of not making, not making a type, type two error. So what are the things that would actually drive power? And to help us conceptualize that, I'll draw two sampling distributions. 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?