How Does Data Science & Statistics: Levels of measurement Work?

Data science and statistics use levels of measurement to help us understand how different types of data behave, like how we sort toys or count candies.

Imagine you have a bag of candies, some are red, some are blue, and some are green. If you just say "red," "blue," and "green," that's like the first level of measurement: nominal. It’s just names, no order, just categories.

Now imagine you line up your candies by size, small, medium, large. That adds a bit more meaning because now we can tell which is bigger or smaller. This is the next level: ordinal. You know the order, but not how much bigger one is than another, like knowing who came first in a race, but not by how many seconds.

If you count your candies and say "I have 5 red candies," that's interval. It tells you how many and the difference between numbers matters (like temperature on a scale where each degree is equal).

And finally, if you know how many candies you have and their weight, like saying "these are heavy candies," that’s ratio, the most detailed level, it has all the features of the others and includes a true zero point, like having no candies at all.

Each level helps us choose better tools to play with our data, just like choosing the right toy for the right game!