Okay, In this video, we're going to contrast one way nova against what's called general linear models. We've already learned in some previous videos that one way ANOVA allows us to test whether or not we have evidence for differences among groups. When we have more than two groups and experiment with one single test. In other words, it allows us with one single test suit to simultaneously look for evidence for differences among groups. And we have more than two groups in an experiment. Okay? One way ANOVA involves a particular form of calculations called ordinary least squares. Okay? And one way ANOVA was invented a while ago, I believe was invented in 1918. And it was invented by Ronald Fisher. And at that time, computers didn't exist. Calculators didn't exist. And so certain approaches were used in order to conduct a one-way ANOVA. Since then, things have changed and new techniques have arisen. And the technique that I'm highlighting in this video is a technique called general linear models. And the particular form of general idea models that I want to draw your attention to is what we call a one-factor general linear model. And a one fact, the general linear model allows us to do exactly the same thing as a one way ANOVA allows us to simultaneously examine the evidence for differences in mean values among multiple groups. I'll say that up here I could, I could have called this a one-factor ANOVA as well as opposed to a one way ANOVA. There's different ways of expressing this, okay? So the point here is that a one-factor general linear model allows us to perform exactly the same task as one way ANOVA, but the calculations are performed differently. Instead of using ordinary least squares, a general linear model does something called a optimizing what's called a likelihood function. I want to emphasize that general linear models are a broad set of tools that allow us to do a number of different things. And general linear models include one way analysis of variance, okay? But as a general tool, general linear models are more general than just the application that we would use to perform a one-way ANOVA, okay, or to perform an analysis that would be equivalent to one way ANOVA. So what I'm trying to say here rather to OT rather awkwardly, is it general linear models involve a general set of tools that allow us to do a number of different things. Including perform an analysis that is equivalent to one way ANOVA. Okay? And in fact, when we perform our analyses in this course, we'll be using that general linear models for our analyses. Having said that, I want to point out that I do have some videos where I explain how the tests that we're using work. And when in the videos that I am, in the videos where I explain how to analyze data that could be analyzed with a one-way ANOVA or with a one-factor general linear model. I choose to explain how these tests work from the perspective of a one way ANOVA even know. In fact, we're going to be performing one factor, general linear models. Okay? And what I want to really address in this video is why, why am, why have I decided to explain these types of analyses from the perspective of a one-way ANOVA instead of versus a one-factor general linear model. First of all, I want to point out at these approaches are incredibly similar. Okay? Well, we've already said that the, the mechanics differ slightly in what we'll get to that in a moment. But really these approaches are incredibly similar, okay? In fact, these two approaches of one-way ANOVA or one-factor general any model, they will yield the same results, okay? And in fact, the function within R that you would use to perform a one-way ANOVA. It's called an AOV. So analysis of variance, this function actually uses code from the function lm. This is the function that we would use to perform a general linear models. So within are the codes that we use to run analysis of variance comes from this function where he performed general idea models. Okay? So these approaches are incredibly linked. Okay? But I'm choosing to explain how these tests work from the perspective of one way ANOVA. Because that perspective is a little bit more tangible. So we can explain how this test works from the one-way ANOVA perspective. Very simply because this perspective just involves a very simple math. So adding numbers, multiplying them, dividing. Okay? In contrast, if I wanted to explain how general linear models work, that approach is slightly more abstract. It's still very accessible. I'm sure everyone to be able to understand it easily. It's just slightly more abstract and my goal is to develop your intuitive understanding for how these tests work. And I want to do that. I want to take the simplest approach possible. And that's why I'm explaining this general type of analysis from the one-way ANOVA perspective. Okay. So just to kind of end this off and this video, I'm just going to highlight that when we perform a one-way ANOVA test in our analyses using R, we are in fact going to be running one factor, general linear models, some case, well, I might be using those terms interchangeably, but I want you to know that the code that you actually running involves one factor, general idea models, even if I'm sometimes calling them one way ANOVA. Having said that, one to explain how these models work, adopting the perspective from a one-way ANOVA. Just because I think it's slightly easier to digest. And as a result, I think it will help give you an intuitive understanding for how these tests work slightly more easily. I'm going to end the video there. And I'll say hope it's been helpful and thank you very much.