The difference between 10 and 0 is also 10 degrees. 20 degrees C is warmer than 10, and the difference between 20 degrees and 10 degrees is 10 degrees. This allows you to measure standard deviation and central tendency.Įveryone's favorite example of interval data is temperatures in degrees celsius. Interval data is fun (and useful) because it's concerned with both the order and difference between your variables. We just know that likely is more than neutral and unlikely is more than very unlikely. See, we don't really know what the difference is between very unlikely and unlikely - or if it's the same amount of likeliness (or, unlikeliness) as between likely and very likely. "How likely are you to recommend our services to your friends?" Have you ever taken one of those surveys, like this? Ordinal scales are often used for measures of satisfaction, happiness, and so on. Not so much the differences between those values. The key with ordinal data is to remember that ordinal sounds like order - and it's the order of the variables which matters. 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 Common examples include male/female (albeit somewhat outdated), hair color, nationalities, names of people, and so on.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).Ĭontinuous 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.ĭiscrete 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? It’s a beautiful system that works in harmony and makes everything more efficient and tidier.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.īecause let's face it: not many people study data types for fun or in their real everyday lives.
If it’s an integer it won’t be required to have decimals, if it’s a string math operations won’t be performed on them etc. computer figures out certain treatments and processes based on data type. Thanks to the data types, your computer knows where data should be stored, how much it should allocate and what kind of operations it should expect concerning data processes.Ģ- So if variables (previous Python lesson) are containers for raw data data type is more like the fundamental categories that has sections in the fridge like veggies, fruits, meat and diary.ģ- So, just as you know if it’s a fruit in the container, refrigeration might not be needed, if it’s meat, you might have to freeze it for long-term use etc. Well, sometimes delicious.) So if it’s going to help you remember think of memory as the fridge and CPU as the oven.
In other words memory stores data and cpu processes them (cooks, cleans, chops and prepares a delicious meal. So why does Python need data types at all? This question may arise to the beginner programmer.ġ- When you think about the kitchen of your computer, memory makes allocation and cpu makes executions. Here are some of the reasons why with a few interesting analogies. It’s not only Python but pretty much any programming language that makes use of data types.
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