Okay, in this video, I'm going to demonstrate how to perform a power analysis. Specifically going to show you how to perform a power analysis for a one-way ANOVA. And we're going to assume that we're going to perform an a priori power analysis. There are two general methods for performing power analyses. One is to use simulations which are useful when you have complex experimental designs. In this case, where we can analyze our data using a one-way ANOVA situation is far too simple to warrant simulations. And so we're going to use existing software. And the software that we're going to use is g power, which is freely available at this website. It's available both for Mac and Windows. And website also provides tutorials to help you learn to use g power. And my demonstration will hopefully show you that g power is a very straightforward piece of software. So when you first open G power, you'll get a window that looks something like this. And to perform the power analysis or to give g power the information that it needs to perform the power analysis. Basically, our job is going to involve choosing the appropriate options in these tabs and fill in the appropriate values in these windows at the bottom. By clicking on this tab in the middle, we can choose a type, a power analysis we want to perform. And we said at the beginning we want to perform an a priori power analysis. And so that's what we've chosen. And we've also said that we want to perform a power analysis for one-way ANOVA. And so we would, in order to do that, we would click on our test family and choose F tests. Because one way ANOVA involves calculating F statistic. So once we've chosen those, now we're going to choose the specific statistical tests we want. And so we would go to this tab here. And once we click on it, I want to point, you can see that there are many options available within the F-test family. You can do linear multiple regression, you can do multivariate analysis of variance or ANOVA. You can do power analysis for repeated measures ANOVA. We just want to do a simple one way ANOVA with which we can specify just by clicking on this option here. Once you've done that. Once you've done that, our job now is just to fill in these bottom windows at the bottom. And I'm going to start just by pointing out what we could put in these bottom three windows. So for this window here, we can specify alpha, which is our error probability, or it's our type one error, which in biology is typically set to 0.05, which is why I have that here. But you could specify something more stringent if you wish, like 0.005, as some authors have suggested. In the second box in the bottom, we can specify the power. And I fill this in with a power of 95% here, which would generally be considered a very high-powered experiment. And this last window here is where we specify the number of groups in our one-way ANOVA. In other words, we work, we want our one way ANOVA to be comparing means or to be able to distinguish a significant effect when we have five different treatment types within our one-way analysis of variance. Now we're going to focus on calculating our effect size, which in this case is designated F, which is the effect size, the colon termed f. And when to fill this in. And we could simply fill in an effect size based on some recommendations, such as Cohen's recommendations for what's considered a small, medium or large effect size. Or we could click on this determined button to allow us to give more specific details. And that's what we're, that's what I'm going to illustrate here. So if I click on this determined button, then a new window will pop out on the right. And you can see that we can calculate our effect size based either on mean values for each of our different groups or based on variance. And I'm just going to demonstrate how to do this when we wants to specify the mean values for our various groups. And so I've clicked on that. And once we've specified this, we can specify the amount of variance that we expect to have within each group. One of the assumptions for one-way ANOVA is that the variance within each group is equal. And so that's why there's only one option here for specifying the standard deviation within the groups. Again, just because the analysis assumes that this standard deviation is equal for all of our groups. Now our job is just to fill in our expected mean value for each of our groups. So when we specified our group number five here earlier, that caused g power to give us five different groups. Or gives us the option to fill in a mean value for five different groups. And I've just said, we're going to imagine that the mean value for our first group is equal to five. Second group is equal to 5. So there'd be no difference between these first two groups. But the next three groups, we'll all have a mean value of equal to six. And once we've done that, we can press the button down here, which is calculating transfer the main window. And then our effect size will appear. Or the appropriate effect size based on these means will appear in this window. And once we've done that, we can just press return and g power will return these values here. And you can see that it gives us a total sample size of 65 in order for us to have 95% power given the means that we've specified. And since we have five groups, this total sample size is 65, means that we should expect to have a sample size of 13 within each of our groups. So that's how you can perform power analysis using g power for a one-way analysis of variance. This, you may want to do an a priori power analysis. I hope this video has been useful and thank you very much.