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. Anything less than negative one, if we did negative n minus 1.05, or n minus 1.5, we start overestimating the variance. Anything less than negative one, so if we have n plus zero, if we divide by n, or if we have n plus.05, or whatever it might be, we start underestimating, we start underestimating the population variance. And you can do this for samples of different sizes.