 One of the things I really love about Chamomile is that they include some really unusual or specialty tests, but make them really easy to use. One of these is McNamara's test. And this is a test designed for contingency tables. But instead of being independent variables, it's for repeated measurements. Now I have to admit, I've been doing research for 30 years and I've never had to do this. But it's always good to know that when you come up against this kind of question, you can get the right tool, which gives you greater power and precision in your research. So McNamara's test is for categories, and it is for frequencies in those categories, but where the categories are repeated. Now what I'm doing here is a little bit different. In my other examples, I've had one row per observation and so I've had these long data sets. Right now I'm using a summary table and it's kind of nice that Jim Ovey lets you do this in a number of situations. That way I can do it this way instead of having like 85 rows of data. The data that I'm using actually comes from a table that I saw on the Wikipedia article for McNamara's test. And it's about patients with Hodgkin's disease and whether they and their siblings had tonsillectomies. I don't know the association between the two. But it's an interesting example because the sibling goes with the patient. So there's a connection between the two of them. Let me show you how we set this up using McNamara's test in Jim Ovey. We come to frequencies and we come down right here. They call it McNamara test. If you go to Wikipedia's McNamara's possessive test, I'm going to click on that. And what we need to do is simply take our data, put in what variable has the row labels, what variable has the column labels, and then the variable that has the counts or the actual frequencies themselves. That's because I'm setting this up as a summary table. So I'm going to take patient and put that in rows. And you can see how it starts to fill it in right here. And I'm going to take sibling and put it into columns and it fills in the table, but it doesn't have any values yet, except to say that in theory, there's just one of each. But then I take the N, that's a variable that tells you how many cases there are in each category. And I put that right here, and it fills in the table. And it does a couple of things. Number one, it gives us that there are 37 cases where the patient with Hodgkin's did not have a tonsillectomy and neither did the sibling. But there are 26 where they both had tonsillectomies. And so there's an association in that way. We also have the actual result here. McNamara's test is based on the value of chi squared. And it's interpreted in the same way that if the value of P, the probability value or the probability of getting the observed effect size if the null hypothesis is true is 0.088. Now that doesn't meet the standard levels of statistical significance, but it's in the direction. I do want to show you a couple of other things we can do that can be really helpful. And that is anytime you're dealing with a table, and especially when you have samples of different sizes, now you see we have 44 Hodgkin's patients that did not have tonsillectomies and 41 that did, those numbers are really close to each other. So they're easy to compare. But for the siblings is 52 and 33. And that makes the comparison a little more difficult. And so what you can do are get row and column percentages. Now, let's do just row percentages first. What this means is that for the patients that did not have a tonsillectomy, 84% of their siblings also did not have a tonsillectomy, whereas 16% did. So they add up to 100% going across. And that's going to be true all the way across. So this really just tells us that 61.2% of the siblings did not have tonsillectomies and 38.8% did independent of that, you can get column percentages. I'm going to turn off the row percentages for just a moment and we'll get column percent. And now what this does is it adds up to 100% going down. And this is what's really nice when you have different overall frequencies. So we have just 52 people without tonsillectomies, 33 that did. And so we can say that of the siblings that did not have a tonsillectomy, 71.2% had patients siblings who did not and 28.8 had patient siblings who did. And we can compare that to the percentages over here. And that allows us to make a sort of an apples to apples comparison, even though the total frequencies are different, because often you're more concerned about the rates. And that's something you can do with any kind of chi-squared table. You can do it in the binomial, you can do it in the chi-squared test for goodness of fit for the chi-squared test of associations. That getting the percentages is often easier to interpret. And the same thing is true in Mechnomar's test, which allows you to do a contingency table for paired data.