# Statistical data types

Statistical data types are the categories of data used in statistics.

**Numerical data**, also known as **quantitative data**, consists of discrete or continuous data. **Discrete numerical data** are values that can be counted, such as a quantity; for example, the number of heads in 10 coin flips or the number of correct answers on a test. **Continuous numbers** are measurements in which the value may fall in between any two other values; for example, the speed of a car or a volume of water.

A car that travels from 0 to 60mph reaches all possible speeds between 0 and 60mph, which makes speed data continuous. Whereas the number of laps a car has taken on a racetrack increments by one each time, thus the number of laps is never between, for example, 1 and 2, and thus "number of laps" is a discrete value.

**Categorical data**, also known as **qualitative data**, consists of data which describes characteristics and does not have a mathematical meaning. For example: Gender, color, and favorite food are all types of categorical data. Though numbers can be assigned to categorical data, it is not valid to compare those numbers mathematically.

**Ordinal data** consists of a mix of categorical and numerical data, where the data fall into numerical categories. For example: a 5-points scale for product reviews is ordinal because each category is numeric and can be compared (e.g. to get an average rating).

## Broader Topics Related to Statistical data types

### Statistics

The analysis of numerical data

### Data

Facts, statistics, and references to information