Now, the whole point that we do or the main thing we do when we do significance tests is we say, all right, if we assume the null hypothesis is true, what's the probability of getting a sample mean this low or lower? And if that probability is below a preset significance level, then we reject the null hypothesis and it suggests the alternative. But in order to figure out that probability, we need to figure out a test statistic. Sometimes we use a z-test. If we're dealing with proportions. But when we deal with means, we tend to use a t-test. And the reason why is if you wanted to figure out a z-statistic, what you would do is you would take your sample mean, subtract from that the assumed mean from the null hypothesis.