All of these things that I just mentioned, these all just mean the standard deviation of the sampling distribution of the sample mean. That's why this is confusing, because you use the word mean and sample over and over again, and if it confuses you, let me know, I'll do another video or pause and repeat it, whatever. But if we just take the square root of both sides, the standard error of the mean, or the standard deviation of the sampling distribution of the sample mean is equal to the standard deviation of your original, of your original function, of your original probability density function, which could be very non-normal, divided by the square root of n. I just took the square root of both sides of this equation. I personally, I like to remember this, that the variance is just inversely proportional to n, and then I like to go back to this, because this is very simple in my head. You just take the variance divided by n. Oh, and if I want the standard deviation, I just take the square roots of both sides, and I get this formula. So here, the standard deviation, when n is 20, the standard deviation of the sampling distribution of the sample mean is gonna be one. Here, when n is 100, well, our variance, our variance here, when n is equal to 100, so our variance of the sampling mean of the sample distribution, or our variance of the mean, of the sample mean, we could say, is going to be equal to 20.