The second type of data that we're going to talk about is categorical data, and this is data that has no inherent numeric meaning.

Most of the time, you can't really compare one category to another directly. Things like gender, yes/no questions, race, state of residence, product category, political party; you can assign numbers to these categories, and often you will, but those numbers have no inherent meaning.

So, for example, I can say that the area of Texas is greater than the area of Florida, but I can't just say Texas is greater than Florida, they're just categories. There's no real numerical quantifiable meaning to them, it's just ways that we categorize different things.

Now again, I might have some sort of numerical assignation to each state. I mean, I could say that Florida is state number 3 and Texas state number 4, but there's no real relationship between 3 and 4 there, right, it's just a shorthand to more compactly represent these categories. So again, categorical data does not have any intrinsic numerical meaning; it's just a way that you're choosing to split up a set of data based on categories.