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STATS for TWITS

1. Getting Started ...

Different kinds of data

The type of data you have determines what sort of test you apply and so you need to know what kind yours is. It is by far the best policy to decide what sort of data you will be collecting before you actually go out and collect it. (See "Advice to distressed non-statisticians").

As far as we are concerned there are three kinds of data.

Interval Data

These are measurements of things like weight and length. A length of 10m it is 2m smaller than a length of 12m. If you can say how far apart one measurement is from another, then you have interval level data.

Ordinal Data

You might have measurements where you do not know how far apart one measurement is from another. For instance let's say you are concerned with facial topiary in humans. You don't want to upset your subjects by measuring directly so you invent a scale of beardiness thus:

1 = naked chin ...2 = stubbly...3 = toothbrush...4 = medium hair...5 = very hairy...6 = badger concealingly hairy

You cannot say that level 4 is twice as beardy as level 2 but you can put your measurements in order. They are ordinal level measurements.

Categorical Data

Quite often in biology you might be concerned with putting things into categories. For example it might be that you are interested in testicle possession in humans. You can put each human into one of two categories: Either with testicles or without testicles. (There is the possibility of a third category of uni-testicular individuals but let us ignore them). You could then use this data to prove that the mean number of testicles per human was one.

Or maybe you are looking at shore crabs and their parasitic barnacle Saculina. You would have two categories: With the parasite and without the parasite.

Matched and Unmatched Data

Your data is matched if a piece of data from one set goes with only one piece of data from the other set. For example you might be measuring temperature of the sea with depth. A specific temperature recording would only be associated with one specific depth.

Your data is unmatched if there is no reason to associate a piece of data from one set with any particular piece of data from the other set. For example you might be measuring the heights of vegetation on trampled and untrampled parts of a path. There is no connection between any of the measurements from the trampled part and the untrampled part.


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