And one thing that we're going to explore further in a future video is not only is it more normal in its shape, but it's also a tighter fit around the mean. And you can even think about why that kind of makes sense. When your sample size is larger, your odds of getting really far away from the mean is lower, because it's very low likelihood if you're taking 25 samples or 100 samples that you're just going to get a bunch of stuff way out here, or a bunch of stuff way out here. You're very likely to get a reasonable spread of things. So it makes sense that your mean, your sample mean, is less likely to be far away from the mean. We're going to talk a little bit more about that in the future. But hopefully this kind of satisfies you that, at least experimentally, I haven't proven it to you with mathematical rigor, which hopefully we'll do in the future, but hopefully this satisfies you at least experimentally that the central limit theorem really does apply to any distribution.