Several types of research require data-crunching. And, whenever data forms the central focus of the research, statistics enter the scene most naturally. The users who need to apply research methods can efficiently use statistical data if they understand how to categorize them and their underlying utilities. So, here is a quick overview of the types of statistical data.
Statistical data is broadly classified into – categorical data and numerical data. These classifications are further divided as follows.
**Categorical Data sub-types**
There are two sub-types of Categorical data, such as:
**Numerical Data sub-types**
**To sum up,**
Understanding statistical data types is important. It helps ascertain the applicability of any statistical procedure. The users can employ their knowledge of statistical data to define the type of research they wish to do.

**Categorical data**

**Nominal Data:**It comprises discrete units that can be labeled uniquely. By labeling, it means that the data has a certain unique value that will always remain unaltered. For example, in a survey, where researchers ask, “Are you married?” The answer is just ‘no’ or ‘yes.’ Thus, a person’s civil status can have one discrete value under the nominal data category. The order of options can be changed, and the result will remain unaffected by changing the order.**Ordinal Data:**When an element of orderliness is included in the nominal data, it becomes ordinal data. So, the only distinguishing factor is that the order of options has some significance. It may be showing a progression or regression of some kind. For example, when you ask ‘What is your level of education?’, you will prefer to keep options as:

**Numerical Data**

**Discrete Data:**The values will be presented in a countable manner in this type of statistical data. Of course, the data will be discrete. The researcher will place the information under numerical data only when it is possible to classify two sets as more, few, or equal and not smaller, bigger, or equal.**Continuous Data:**Only measuring of data is possible; counting is not. For example, you can only measure the volume; you can’t count it. A height of a person is another continuous data example that can be marked on a measuring scale. Even Continuous Data is possible to divide further into Interval data and ratio data.