And what's interesting about this, I guess it's different than almost every other distribution we've looked at, although we've looked at others that have this property as well, is that you can't have a value below 0, because we're always just squaring these values. Each of these guys can have values below 0. They're normally distributed. They could have negative values. But since we're squaring and taking the sum of squares, this is always going to be positive. And the place that this is going to be useful, and we're going to see in the next few videos, is in measuring essentially error from an expected value. And if you take this total error, you can figure out the probability of getting that total error if you hold some parameters, or if you assume some parameters.