 Chapter 8 hypothesis testing is the big chapter in our book. It's the longest, it has the most ideas, and truthfully, it has logic that can be really confusing if you're not already acclimated to it. What we're going to be talking about in this chapter is one of the most common procedures for analyzing data, for doing inferential statistics. We're going to talk about things like effect sizes, how big is the difference between two groups, including summarizing it with a measure of effect size called Cohen's D, I think difference. We'll also talk about probability values, or P values, then we'll talk about the logic of hypothesis testing, which it's full name is null hypothesis significance testing. And it lets you know that you're going to be dealing with something called a null hypothesis that says nothing is going on, there's nothing to be worried about, or an alternative hypothesis which says something's happening, and it triggers something, or it lets you know that this group is in fact fundamentally different than the other group. We'll also talk about the kinds of mistakes that can happen, a type one or false positive and a type two or false negative. And then we'll finish by going through our first hypothesis testing procedure, the one sample Z test, where you get a sample mean and try to decide whether that mean is sufficiently different from the expected population mean for you to decide that something's happening. Now, in terms of why we study this, well, the really obvious reason is that hypothesis testing is really common in psychology, sociology, anthropology, the social and behavioral sciences. It's the basis of the remaining chapters in our book, 8, 9, 10, 11, 12 are all about hypothesis testing different kinds. It's very common in professional research and psychology. It's actually really good anytime you have to make a yes-no decision or a go-no-go decision because what it does is it takes this data that's maybe based on a mean and so that's something that's fundamentally continuous and it gives you a cutoff point. If it's below this, we don't worry about it. If it's above this, we do something. Now, in terms of what you can use this for, sort of the practical real-life things, people do this on both a explicit and an implicit method all the time. If you're a therapist and you have somebody come in, you're trying to make a diagnosis. You go through the diagnostic criteria maybe in the DSM and on the basis of their symptoms, you try to decide whether they have a particular disorder. The no-hypothesis is that, no, they're fine. They're just having a bad day or they're stressed out right now. But if it goes beyond a certain point, you say, no, this isn't just stress. This is a major depressive disorder. Or think about legal situations. In a criminal trial, people present evidence and the jury thinks about that evidence and the standard is a person's innocent until proven guilty. So the no-hypothesis is that there's nothing there and that, yeah, maybe weird things have happened, but it's only when the jury believes that the evidence has taken it past a certain point that they might convict somebody. Or in a less formal situation, try to think about when you're trying to decide if somebody's treating you fairly or if maybe you think you're being unfairly discriminated against. On the basis of your interactions with that person or group of people, you assume things are basically good, but if you have enough interactions where there are problems, there will come a point where you decide there is something going on here. And if it's a personal relationship, maybe you talk to the person about it. Maybe you just don't spend time with them anymore. If it's at work, maybe you talk to your boss or you even file a complaint with human resources. But this is a process that you go through all the time, either formally or informally. Again, it's something that people use daily and it's also something that forms the basis of so much of psychological research. And that's why we make hypothesis testing such an important part of this course.