But let's say our actual results, when we look at these 100 items, we get that A is the correct choice 20 times, B is the correct choice 20 times, C is the correct choice 25 times, and D is the correct choice 35 times. So if you just look at this, you say, hey, maybe there's a higher frequency of D. But maybe you say, well, this is just a sample, and just a random chance, it might have just gotten more Ds than not. There's some probability of getting this result, even assuming that the null hypothesis is true. And that's the goal of these hypothesis tests. They say, what's the probability of getting a result at least this extreme? And if that probability is below some threshold, then we tend to reject the null hypothesis and accept an alternative. And those thresholds you have seen before, we've seen these significance levels.