So you could view it as just the probability of not making a type two error or one minus the probability of making a type two error. Hopefully that's not confusing. So let me just write it the other way. 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.