I'm going to draw it a little bit narrow, because we learned from the central limit theorem that the standard deviation of this thing, that the standard deviation, so let me draw the mean. So the population mean of the sampling distribution is going to be, we're going to denote it with this X bar. That tells us the distribution of the means when the sample size is n. And we know that this is going to be the same thing as the population mean for that random variable. And we know from the central limit theorem that the variance of the sampling distribution, or often called the standard error of the mean, is going to be equal to the population variance divided by this n right over here. And if you wanted the standard deviation of this, you just take the square root of both sides. Now let's do the same thing for random variable y. So let's take the sampling distribution of the sample mean, but here we're talking about y, random variable y.