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. Let me try a sample size six. And here you go, once again, as I press, I'm just keeping generate sample pressed down. As we generate more and more and more samples, and for all of the a's, we essentially take the average across those samples for the variance, depending on how we calculate it, you'll see that, once again, our best estimate is pretty darn close, is pretty darn close to negative one.