Types of Data and Variables
Before performing any statistical analysis, it is very important to understand the type of data we are working with. Statistics does not treat all data in the same way. The methods we use depend entirely on the nature of the data.
In this lesson, we will understand what variables are and how data is classified in statistics, step by step, using simple explanations.
What Is a Variable?
A variable is any characteristic or attribute that can take different values from one observation to another. If a value can change, it is considered a variable.
For example, the age of people in a group will not be the same for everyone. Similarly, income, height, exam marks, or even the color of a product can vary. All of these are variables.
In statistics, variables are what we measure, observe, or record when collecting data.
Main Classification of Data
In statistics, data is broadly classified into two main types based on how the values are represented.
The two main types of data are:
- Qualitative data (also called categorical data)
- Quantitative data (also called numerical data)
Understanding this distinction is critical because each type of data is analyzed using different techniques.
Qualitative (Categorical) Data
Qualitative data represents characteristics or categories that cannot be measured using numbers. This type of data describes qualities rather than quantities.
Examples of qualitative data include gender, color, country, brand name, or type of device. These values tell us what kind of category something belongs to.
Even if numbers are used as labels, such as assigning numbers to categories, mathematical operations like addition or averaging do not make sense for qualitative data.
Types of Qualitative Data
Qualitative data can be divided into two types based on whether the categories have a natural order.
Nominal Data
Nominal data represents categories that have no natural order. The categories are simply labels used for identification.
Examples include blood group, nationality, product type, or operating system.
Ordinal Data
Ordinal data represents categories that do have a meaningful order or ranking. However, the difference between the categories cannot be measured numerically.
Examples include customer satisfaction levels such as low, medium, and high, education level, or survey ratings.
Quantitative (Numerical) Data
Quantitative data represents numerical values that can be measured or counted. This type of data allows mathematical operations such as addition, subtraction, and averaging.
Examples of quantitative data include age, salary, distance, number of items sold, or test scores.
Most statistical calculations are performed using quantitative data.
Types of Quantitative Data
Quantitative data is further divided based on how the values are obtained.
Discrete Data
Discrete data consists of countable values. These values are usually whole numbers and represent counts.
Examples include the number of students in a class, number of defective items, or number of calls received.
Continuous Data
Continuous data consists of measurable values that can take any value within a range, including decimal values.
Examples include height, weight, temperature, time, or distance.
Why Data Types Matter
Correctly identifying data types is essential in statistics because it affects how data is analyzed and interpreted.
For example, calculating an average makes sense for numerical data such as age or income, but it does not make sense for categorical data such as colors or countries.
Choosing the wrong method for a data type can lead to incorrect conclusions.
What’s Next
In the next lesson, we will study Populations, Samples, and Parameters. This lesson will explain how data is collected and how sample data is used to draw conclusions about a larger group.