 The way Jmovi is set up, when you first go to explore your data, it offers you descriptive statistics or a numerical insight into your data. On the other hand, I actually prefer to begin with pictures, graphics, visualizations, do those first and then get numbers to provide precision that's in addition to what you get from the graphics. So in this one, I'm going to show you the first of several different visualizations that we get from Jmovi. The first one is histograms. Now the data set that I have open is the iris data. This is one of the example data sets. I'm going to come over here to exploration, click on that and go to descriptives. Now, by default, it's going to try to do statistics. I actually don't want statistics. So I'm going to preempt that and just remove this information right now. So it's not going to produce a table. So the table is gone. What I am going to do however is I'm going to select these four quantitative variables. These are measurements of the pedals and sepals from three species of irises. I'm going to put those in variables. Now right now it's doing nothing because I canceled all of the statistics. But I need to come to this menu which has plots. And all I'm going to do is select the first one, which is histogram. Remember, a histogram is a chart like a bell curve that shows you how common each score is in a distribution. The width of each bar or the bin width is arbitrary. And a lot of other programs you can adjust that manually if you want. You can't do that in Jmovi. But truthfully, right now it just makes your life a little simpler. What we have here is a pretty strong unimodal distribution for sepal length. We have kind of normal with a big spike in sepal width. We have a strong bimodal distribution for pedal length, although it might be two separate normal distributions maybe. And then for pedal width, again, strong bimodal with this really skewed distribution here on the far left. So this lets us know by the way when you get a bimodal distribution that generally tells you that you have more than one distribution happening there that got combined. And that makes sense because in this data set we have three different species of irises. And with that, it makes sense to split this up and look at each of the species separately. So I'm going to do this command again, I'm going to come back up to exploration to descriptives. And then I'm going to select the four variables again, put those into width. But this time, I'm going to add this one, I'm going to put species under split by, I'm going to cancel out these statistics again, those aren't what I'm looking for right now. But I'm going to get histograms. Now what we have are separate stacked histograms for each of the three species. Now, it does a really nice thing in that it puts all of them on the same scale. So for instance, here on sepal length, all three of these go from four centimeters up to eight centimeters, I'm going to click over here to close the menu. And it puts the three different species or three different groups in different colors. So it's really easy to tell apart. And you can see Satosa is the lowest Versa colors kind of middleish and virginica is a little higher. For sepal width, you can see that we've got some big differences there were Satosa is now the highest. And Versa color and virginica are pretty similar to each other, although we have an outlier on Satosa. And then for petal length, we have an enormous difference Satosa is way down there at the end. And the other two are pretty close to each other. And then finally for petal width, you can see the skewed distribution that's really close to zero for the Satosa irises, whereas Versa color is a little bit medium and virginica is at the highest. And so this is a great first step for getting a visual exploration of your data. First by combining all the groups in one. And when you get an indication that maybe there's something going on there, then splitting it up to see if you can drill down and get a little more explanation for some peculiar results like bimodal distributions or skewness. And that's what we get from these separate stacked small multiple histograms, which are very easy to do in Jamobi and really a great way to start breaking down your data.