And all this error means is that you've rejected, this is the error of rejecting, let me do this in a different color, rejecting the null hypothesis even though it is true. So for example, in actually all of the hypothesis testing examples we've seen, we start assuming that the null hypothesis is true. We always assume that the null hypothesis is true. And given that the null hypothesis is true, we say OK, if the null hypothesis is true, then the mean is usually going to be equal to some value. So we create some distribution assuming that the null hypothesis is true. It normally has some mean value right over there. And then we have some statistic, and we are seeing if the null hypothesis is true, what is the probability of getting that statistic, or getting a result that extreme or more extreme than that statistic?