And the key idea here is when you take a sample, your sample mean is always going to sit within your sample. And so there is a possibility that when you take your sample, your mean could even be outside of the sample. And so in this situation, and this is just to give you an intuition, so here your sample mean is going to be sitting someplace in there. And so if you were to just calculate the distance from each of these points to the sample mean, so this distance, that distance, and you square it, and you were to divide by the number of data points you have, this is going to be a much lower estimate than the true variance from the actual population mean, where these things are much, much, much further. Now you're always not going to have the true population mean outside of your sample, but it's possible that you do. So in general, when you just take your points, find the square to distance to your sample mean, which is always going to sit inside of your data, even though the true population mean could be outside of it, or it could be at one end of your data, however you might want to think about it, you are likely to be underestimating the true population variance. So this right over here is an underestimate.