Now this next chart really gets to the meat, really gets to the meat of the issue, because what it's telling us is that for each of these sample sizes, so this right over here, for sample size two, if we keep taking sample size two and we keep calculating the by sample variances and dividing that by the population variance and finding the mean over all of those, you see that over many, many, many trials, many, many samples of size two, that that by sample variance over population variance, it's approaching half of the true population variance. When sample size is three, it's approaching 2 3rds, 66.6% of the true population variance. When sample size is four, it's approaching 3 4ths of the true population variance. And so we can come up with a general theme that's happening. When we use the biased estimate, when we use the biased estimate, we're not approaching the population variance, we're approaching n minus one, let me write this down, we're approaching n minus one over n times the population variance. When n was two, this approached 1 1.5, 1 1.5. When n is three, this is 2 3rds.