When we have high values of a, we are underestimating. When we have lower values of a, we are overestimating the population variance. But that was just for one, that was just for one sample, not really that meaningful. It's one sample of size two. Let's generate a bunch of samples, and then average them over many of them. And you see, when you look at many, many, many, many, many samples, something interesting is happening. When you look at the mean of those samples, when you average together those curves from all of those samples, you see that our best estimate is when a is pretty close to negative one, is when this is n plus negative one, or n minus one.