This is the biased sample variance. So he's calculating it. That is being calculated for each of our data points, so starting with our first data point in each of our samples, going to our nth data point in the sample. 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.