Perhaps eye color would've been a better example. And they're only really related by the main category of which they're a part. For the purposes of statistics, anyway, you can't have both brown and rainbow unicorn-colored hair. Notice that these variables don't overlap. In plain English: basically, they're labels (and nominal comes from 'name' to help you remember). Common examples include male/female (albeit somewhat outdated), hair color, nationalities, names of people, and so on.

Nominal data are used to label variables without any quantitative value. Like the weight of a car (can be calculated to many decimal places), temperature (32.543 degrees, and so on), or the speed of an airplane. It can be divided up as much as you want, and measured to many decimal places. You can't have 1.9 children in a family (despite what the census might say).Д¬ontinuous data, on the other hand, is the opposite. Like the number of people in a class, the number of fingers on your hands, or the number of children someone has. continuous data.Дiscrete data involves whole numbers (integers - like 1, 356, or 9) that can't be divided based on the nature of what they are. There's one more distinction we should get straight before moving on to the actual data types, and it has to do with quantitative (numbers) data: discrete vs. Qualitative means you can't, and it's not numerical (think quality - categorical data instead). In short: quantitative means you can count it and it's numerical (think quantity - something you can count). Quantitative vs Qualitative data - what's the difference?

If you're studying for a statistics exam and need to review your data types this article will give you a brief overview with some simple examples.Д«ecause let's face it: not many people study data types for fun or in their real everyday lives.

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