So let's start with something very simple.

You should have seen something like that in a similar way,

but I think it's always important to go back and start with some basics.

So what we are measuring in the lab is normally variables

as result of our experiments.

And these variables can have different scales.

So you divide two primary scales, which is categorical and numerical.

So let's start first with categorical.

So you can have for categorical unordered or ordered categories.

So if things are not in an order, so you can't give them ranks,

like sex, blood group, hair color, it's called nominal.

And if you are able to assign ranks, like a disease stage,

toxic potency, things like that, then it is called an ordinal scale.

On the other hand, you have the numerical scales,

which are divided into discrete and continuous.

So discrete are things that you can describe in integer numbers.

So if you toss a coin, for example, you can only have one, two, three, four

times see the eagle, but you cannot see it three and a half times, for example.

So in general, you can say counts, things you can count.

On the other hand, continuous variables, the entire space of numbers is a.

Typical things for a continuous variable could be, for example,

OD measurement in the lab, but also height or blood pressure.

There are also several other types of data.

For example percentages, viability in a cell culture after exposure to a toxicant.

Or you can ratios, so the body mass is an example for a ratio.

You can have quotients or rate or scores, or in general, you could also have

censored data, which means your detection has left and right limit,

and you're not able to detect, to measure things which are outside of these limits.

Most of the times,

these kind of variables are simply treated as continuous measurements.

Why I'm saying all this?

Depending on which scale your data is,

you apply different tools and methods in statistics.