And this should hopefully make logical sense because what we're saying is, hey, look, we took a sample, and if we assume the null hypothesis, the probability of getting that sample is 3%. It's 3 100ths, and so since that probability is less than our probability threshold here, we'll reject it and say we have evidence for the alternative. On the other hand, there might have been a scenario where we do all of the calculations here and we figure out a p-value, a p-value that we get is equal to 0.5, which you can interpret as saying that, hey, if we assume the null hypothesis is true, that there's no change due to making the background yellow, I would have a 50% chance of getting this result. And in that situation, since it's higher than my significance level, I wouldn't reject the null hypothesis. A world where the null hypothesis is true and I get this result, well, you know, it seems reasonably likely. And so this is the basis for significant tests generally, and as you will see, it is applicable in almost every field you'll find yourself in. Now, there's one last point of clarification that I wanna make very, very, very clear.