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. So if you have a sample size of 10 or 20, you're already getting very close to a normal distribution. In fact, about as good an approximation as we see in our everyday life. What's cool is we can start with some crazy distribution. This has nothing to do with a normal distribution. But if we have a sample size, this was n equals 4, but if we have a sample size of n equals 10 or n equals 100, and we were to take 100 of these instead of 4 here and average them and then plot that average, the frequency of it, then we would take 100 again, average them, take the mean, plot that again. And if we were to do that a bunch of times, in fact, if we were to do that an infinite time, we would find that, especially if we had an infinite sample size, we would find a perfect normal distribution.