And so when we see this P sub zero, this is the assumed proportion from the null hypothesis. So that's the difference between these two, the sample proportion and the assumed proportion. And then you'd wanna divide it by what's often known as the standard error of the statistic, which is just the standard deviation of the sampling distribution of the sample proportion. And this works out well for proportions because in proportions, I can figure out what this is. This is going to be equal to the square root of the assumed population proportion times one minus the assumed population proportion, all of that over N. And then I would use this Z statistic to figure out the P value. And in this case, I would look at both tails of the distribution because I care about how far I am either above or below the assumed population proportion. Now with means, there's definitely some similarities here.