The variance of the means, or the variance of our distribution, is going to be equal to the sum of the variances of each of these sampling distributions. And we know that the variance of each of these sampling distributions is equal to the variance of this sampling distribution, is equal to the variance of the population distribution, divided by our sample size, and our sample size in this case is 100. And the variance of this sampling distribution, for our control, let me do this in a new color, for our control is going to be equal to the variance of the population distribution for the control, divided by its sample size. And since we don't know what these are, we can approximate them, especially because our n is greater than 30 for both circumstances, we can approximate these with our sample variances for each of these distributions. So let me make this clear. Our sample variances for each of these distributions. So this is going to be our sample variance 1, or actually our sample standard deviation 1 squared, which is the sample variance for that distribution, over 100, plus my sample standard deviation for the control squared, which is the sample variance, standard deviation squared is just the variance, divided by 100.