 The more you work with data, the more you learn that outliers can be a really serious problem. They can throw off your analyses and your conclusions dramatically. And so you want to pay special attention to the presence of outliers in your data. And probably the easiest way to do this is not with a histogram or with a density plot, but with a box plot. So in this example, which uses the iris data and starts up where we left off with the density plots, I'm going to do box plots and show how they relate to the density plots and the extra insight we can get from them in terms of identifying outliers. Now to do this, I'm just going to click on my existing density plot analysis. And then when that opens up, I'm going to come down here to plots. And I'm going to click box plot. Now you notice this is in a separate column. And what this means is it's going to produce a separate chart. So the density plots aren't going to go away. This will be in addition, but I'll then remove the density plots. So here come the box plots. Scroll over, we'll see that the box plot is directly underneath the density charts. And in the first one, there are no outliers. This one, we get a few outliers on each end. That's not surprising, because if you remember your statistical terminology, this is basically a leptocratic distribution tends to have a lot of outliers. With petal length, we don't have outliers, but that's because of the bimodal nature, the middle part of the box plot is spread out so much. And then the same thing is true down here at the bottom. Again, when it's spread out like that, let's you know something really unusual is going on with your data. I'm going to turn off the density charts in this analysis. And so we'll have just the box plots at this point. Now, you can do the same thing with grouped analyses where you split it by something. So I'm going to close this analysis for a moment. And come down here where we had our stacked density plots showing the data for the three different species. If I click on that, I can do the same thing that I did, I can get a box plot. I'll click box plot. And it's going to show up directly beneath each chart. Now, this time we lose the pretty coloring, but it's easy to see what's going on. We have an outlier for virginica. We have two outliers each for Satosa and virginica, you know, these are pretty symmetrical, they don't look bad. I'm going to come here and turn off the density plots. And so we can have just the box plots. And then we come down to petal with it, we can see we got something really serious going on here because this one is so squished. It's distribution is so narrow. And it's so far away from the others that lets us know that we have something really important going on. And then the petal length the same idea. And so box plots are a good way of identifying outliers, we don't have any massive outliers in this data set, which tells us that we're in pretty good shape. But what we do have are strongly differing distributions. On the other hand, if you're doing something like the analysis of variance, that's kind of what you're looking for. And this just confirms our insight that the three species of iris differ significantly in some of their measurements, the box plots are a good way to check that. And again, a good way to check for the potential influence of outliers in the data.