# Types of data

Different data require different methods of summarising, describing and analysing.

There are four main types of data: Nominal, Ordinal, Interval and Ratio. It is important to be able to identify which type of data you have in order to choose appropriate statistical methods. Take a look at the examples below for a quick refresher of the four types of data:

When reading about different types of data, you might find that different terminology is used. The boxes below introduce additional statistical terminology that is used to summarise the types of data detailed above.

## Categorical

Categorical data is named data that can either be ordered or unordered. Nominal and ordinal data are often referred to as categorical data.

## Scale

Scale data is used to describe numerical data that has a meaningful scale. Interval and ratio data are often referred to as scale data.

## Discrete

Discrete data only takes certain values. For example, the number of patients in a trial, the role of a dice 1, 2, 3, 4, 5 or 6.

### Continuous

Continuous data is numbered data that can take any value within a range. Examples include, height (cm) weight (kg) and race times (seconds).

## Quantitative

Quantitative data is data that can be quantified. This can be as simple as reporting a percentage of yes to no responses or more complex, reporting if results are statistically significantly different. These pages focus on quantitative data.

## Qualitative

Qualitative data refers to data that represent opinions. This data cannot be captured as a quantity, but can be used to provide deeper insight into a topic. Examples of qualitative data are open-ended questions, responses from focus groups, interviews and observations.

Once you are confident in identifying the type of data you have, take a look at the descriptive statistics page for ideas on how to present and summarize your data set.