And the central limit theorem told us that, assuming that we have a sufficient sample size, this thing right here, this thing is going to be the same thing as the standard deviation of our population divided by the square root of our sample size. So this thing right over here can be rewritten as our sample mean minus the mean of our sampling distribution of the sample mean divided by this thing right here, divided by our population mean divided by the square root of our sample size. And this is essentially our best sense of how many standard deviations away from the actual mean we are. And this thing right here, we've learned it before, is a z-score. Or when we're dealing with an actual statistic, when it's derived from the sample mean statistic, we call this a z statistic. And then we could look it up in a z-table or in a normal distribution table to say, what's the probability of getting a value of this z or greater? So that would give us that probability.