Before starting the analysis of a dataset, it is important to pay close attention to the type of data you are dealing with. Depending on the type of data, different statistical techniques should be used to describe and analyze the dataset. It is therefore essential to be able to correctly identify the type of data you are working with.

In statistics, there are two basic types of variables: qualitative and quantitative variables.
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Due to its non-numeric nature, no mathematical operations should be applied to qualitative data. This drastically limits the number of statistical techniques that can be used to describe and analyze qualitative data.

One potential pitfall of dealing with qualitative data is that verbal labels (words/letters) are quite often encoded as numerical values. A common example is gender, which is often encoded using integers such that #\text{male} = 1#, #\text{female} = 2#, #3 = \text{other}# (or some other combination). It is important to note that, despite the use of numerical values to encode the data, no meaning should be attributed to these numbers. For example, it would be incorrect to state that because males are coded as #1# in the dataset and females as #2# females have twice as much gender as males.
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Quantitative variables can be further subdivided into two types: discrete and continuous variables.
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