But it turns out if I were to plot the sample, 10,000 of the sample means here, I'm going to have something that, two things, it's going to even more closely approximate a normal distribution, and we're going to see in future videos it's actually going to have a smaller, well, let me be clear, it's going to have the same mean. So that's the mean, this is going to have the same mean. But it's going to have a smaller standard deviation. So I want to, well, I should plot these from the bottom because you kind of stack it. One, you get one, then another instance, then another instance. But this is going to more and more approach a normal distribution. So the reality is, and this is what's super cool about the central limit theorem, as your sample size becomes larger, or you can even say as it approaches infinity, but you really don't have to get that close to infinity to really get close to a normal distribution.