Statistics is defined as the science of **Collecting, Organizing, Summarizing, Analyzing, Presenting**, and **Interpreting the data**. On the basis of this data analysis valid conclusions and making reasonable decisions are derived.

Statistics are the result of data analysis. The two processes of data analysis are interpretation and visualization. In this article, we are going to discuss the different types of data in statistics in detail.

Table of Contents

## What are Types of Data in Statistics?

The collected data all boils down to 2 kinds of data at the highest level and 4 sub levels:

- Numerical (or Quantitative) data
- Discrete data
- Continuous data

- Categorical (or Qualitative) data
- Nominal data
- Ordinal data

## Numerical (or Quantitative) Data

Numerical data answers key questions such as “how many, “how much” and “how often”. Numerical data is also called quantitative data. Quantitative data can be **expressed as a number or can be quantified**. Simply put, it can be measured by numerical variables.

Numerical data provides information regarding quantities of a specific thing. Example of numerical data are height, weight, temperature and so on.

Quantitative data can be represented by a wide variety of statistical visualization and charts such as line, bar graph, scatter plot, and etc.

There are two types of numerical variables: **discrete **data and **continuous **data

### 1. Discrete Data

Discrete data can only take certain values and can not be made more precise. The discrete data cannot be subdivided into parts and has a limited number of possible values.

For example, number of students in a class, numbers on a die (any number from 1 to 6) or number of days in a month. These are examples of discrete data because they have fixed points and can not be further divides. We can not get 2.5 on a die nor we can count 1.5 students.

### 2. Continuous Data

Continuous data represents information that can be meaningfully divided into finer levels. Continuous data can be measured on a scale or continuum, can take any value usually within certain limits, and could be divided into finer and finer parts.

For example, your height is a continuous variable as it can be measured at different fractions and scale — meters, centimeters, millimeters and so on. There can be literally millions of possible heights: 172.1762 cms or 172.9483 cms. The continuous variables can take any value between two numbers.

Other examples are temperature, time, speed etc. Time of an event can be measures in years or divided into smaller fractions (months, days, hours, minutes, seconds, and so on).

## Categorical (or Qualitative) Data

Categorical data data give us information about the qualities of things and can’t be measured. They are observed phenomenon, so we generally label them with names. Categorical data is also known as Qualitative data.

There are two types of categorical data, **nominal** data and **ordinal** data.

### 1. Nominal Data

The term ‘nominal’ comes from the Latin word “nomen” which means** **“name”. Nominal data has no intrinsic ordering to its categories.

For example, gender is a categorical variable having two categories (male and female) with no intrinsic ordering to the categories. Other examples are Nationality (Indian, British, American,…) or Hair color (Brown, Blonde, Brunette, Red, etc.)

As observed from the examples there is no intrinsic order to the variables and can not ordered in a rank-wise manner i.e., from highest to lowest or vice-versa.

### 2. Ordinal Data

Ordinal data represents category in a particular order. The order associated with the data is specified using some metric. For example, movie rating of 5 may be highest and 1 is the lowest **or** rank 1 in a race is best and rank 10 is the lowest.

Other examples are, temperature as a variable with three **orderly** categories (low, medium and high).

**Note:** Numbers can be used to specify order in Ordinal data. It will not be considered as numerical (or quantitative data).