 Let's review today's topic of chryscare analysis. We discussed categorical variable versus continuous variables and contingency tables. Now, let me ask you a few questions to see how well you're understanding these concepts. First of all, if I have five groups, let's say of different breeds of dogs, is this a categorical variable or continuous variable? Who can tell me the answer to this question and explain why? Thank you. Yes, that is the correct answer. You can have dogs that are terriers or a German shepherd or a colleague. These are groups of types of dogs and it's a categorical variable. Here's my next question. If I am studying how tall my students are and I measure them in centimeters, is there a categorical variable or a continuous variable and why? Yes, that's right. How does a continuous variable, it can take on any value between its minimum value and its maximum value. A student can be 152 centimeter or 200 centimeter or anything in between. Each centimeter is part of a continuum and it's continuous variable. Here is a final question. Let's say I created a task comprising 20 multiple choice questions and I'll demonstrate that task to 40 students. Then I counted the number of correct answers out of the 20 questions, like 18, 17 or some student got 15 out of the 20. Can I use a contingency table for data analysis in this situation? Yes, no, and why? The answer is yes. If you are counting the number of the right answers, you can certainly use a contingency table. But if you are calculating the percentage of the right answers like 98%, we cannot use a contingency table because you are treating it as a continuous variable. I know it's confusing, but there are different kinds of percentage. We're going to talk about the two different kinds of percentage next time. But for now, what we need to remember is that if you can count the number of categories, we can use a contingency table.