You're taking that data point, subtracting out the sample mean, squaring it, and then dividing the whole thing not by n minus one, but by lowercase n. And this tells us several interesting things. The first thing it shows us is that the cases where we are significantly underestimating the sample variance, when we're getting sample variances close to zero, these are also the cases, these are also the cases, or they're disproportionately the cases, where the means for those samples are way far off from the true sample mean. Or you could view that the other way around. The cases where the mean is way far off from the sample mean, it seems like you're much more likely to underestimate the sample variance in those situations. The other thing that might pop out at you is the realization that the pinker dots are the ones for smaller sample size, while the bluer dots are the ones of a larger sample size. And you see here these two little, the two little, I guess the tails, so to speak, of this hump, that at these ends, you disproportionately, it's more of a reddish color. That most of the bluish or the purplish dots are focused right in the middle right over here, that they are giving us better estimates.