 Chapter 10 in our textbook is the analysis of variants, also called ANOVA or ANOVA. And you can think of this as a way of comparing the means of several different groups. There are actually two versions of ANOVA. There is the one factor or one way where you compare the means of several different groups like people in different majors or people from different states. And there's a two factor or two way ANOVA, which allows you to compare two different variables as predictors and the interaction are really the combination of the both of them affecting a single quantitative outcome. This is where we get to talk about things like main effects, how much of a difference is there by one variable all by itself and interactions. And really interactions are where the magic is in the analysis of variants. So in terms of what we learn, we get the one factor, the two factor, the interactions, main effects. But why we learn this is because the analysis of variants, especially the two factor analysis of variants is enormously useful in psychological research where you're trying to nail down the contingencies or the qualifications on something. If you're trying to do a cause and effect experiment, you're gonna do an analysis of variants to look at when the cause is present or absent and how people respond. Say, for instance, in prime this way or prime that way, looking at the connection between the two of those. And not only is it really common, but it lets you understand, again, the nuances. If you wanna know the what for, why are you learning this? Because you're doing an implicit version of this anytime you're qualifying an answer. So if you think about how much you like ice cream, well, maybe you like it a lot, but you're not gonna like it so much when you're totally full or when it's freezing outside. So that's a qualification, that's an interaction. Your level of satiation or the outside temperature is going to affect and interact with how much you like or dislike something. Or let's say you wanna go camping and you say, you wanna go camping down in Southern Utah. Well, that could be a lot of fun, but it's gonna be a lot more fun if you don't go in the middle of the summer where you're just gonna roast. Go in the spring or the fall and it's beautiful. And so the time of the year qualifies what you're looking at. Now, if you've ever heard people talk about identity and intersectionality, that's an example of what we're talking about with the analysis of variance. When they say it's helpful to know whether a person is white or African-American or Latino or what have you. But it's also important to know whether they're male or female, that the gender and the race or ethnicity interact. They intersect and they create different experiences for people and there's a whole lot more you could put into that. But those kinds of qualifications, again, the contingencies are an important reason why knowing how to think about the interactions and the analysis of variance can make your thinking richer and more useful.