If you looked at every possibility of all of the samples that you can take from your original distribution, from some other random original distribution, and you took all of the possibilities of, let's say sample size, let's say we're dealing with a world where our sample size is 10. If you took all of the combinations of 10 samples from some original distribution and you averaged them out, this would describe that function. Of course, in reality, if you don't know the original distribution, you can't take an infinite samples from it. So you won't know every combination. But if you did it with a thousand, if you did the trial a thousand times, so a thousand times you took 10 samples from some distribution and took a thousand averages and then plotted them, you're gonna get pretty close. You're gonna get pretty close. Now, the next thing I wanna touch on is, what happens is, we know as n approaches infinity, it becomes more of a normal distribution.