Standard deviation is gonna be the square root of one, well, that's also going to be one. So we could also write this. We could take the square root of both sides of this and say the standard deviation of the sampling distribution of the, of the standard deviation of the sampling distribution of the sample mean, it's often called the standard deviation of the mean, and it's also called, I'm gonna write this down, the standard error of the mean, standard error of the mean. 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.