Data Types in Statistics

Data is a simple record or collection of different numbers, characters, images, and others that are processed to form Information. In statistics, we have different types of data that are used to represent various information. In statistics, we analyze the data to obtain any meaningful information and thus categorizing data into different types is very important. Data types in statistics help us to make an informed decision about what type of process is used to analyze the data.

Here, in this article, we will learn about types of data in statistics in detail, examples, and others in detail. Before learning about data let’s first learn about Data.

What is Data?

Data is defined as the collection of numbers, characters, images, and others that can arranged in some manner to form meaningful information. In statistics, the data is mainly the collection of numbers that is first studied then analyzed and presented in some way that we can get some meaningful insight from that data.

For example, 12, 19, 17, and 15 this is data.

What are Types of Data in Statistics?

The data in statistics is classified into four categories:

• Nominal data
• Ordinal data
• Discrete data
• Continuous data

Data Types In Statistics

In statistics, there are four main types of data: nominal, ordinal, interval, and ratio. These types of data are used to describe the nature of the data being collected or analyzed, and they help determine the appropriate statistical tests to use. In this essay, we will explore each type of data in detail, providing examples along the way.

Qualitative Data (Categorical Data)

As the name suggest Qualitative Data tells the features of the data in the statistics. Qualitative Data is also called Categorical Data and its categories the data into various categories. Qualitative data includes data such as gender of people, their family name and others in sample of population data.

Qualitative data is further categorized into two categories that includes,

• Nominal Data
• Ordinal Data

Nominal Data

Nominal data is a type of data that consists of categories or names that cannot be ordered or ranked. Nominal data is often used to categorize observations into groups, and the groups are not comparable. In other words, nominal data has no inherent order or ranking. Examples of nominal data include gender (Male or female), race (White, Black, Asian), religion (Hinuduism, Christianity, Islam, Judaism), and blood type (A, B, AB, O).

Nominal data can be represented using frequency tables and bar charts, which display the number or proportion of observations in each category. For example, a frequency table for gender might show the number of males and females in a sample of people.

Nominal data is analyzed using non-parametric tests, which do not make any assumptions about the underlying distribution of the data. Common non-parametric tests for nominal data include Chi-Squared Tests and Fisher’s Exact Tests. These tests are used to compare the frequency or proportion of observations in different categories.

Ordinal Data

Ordinal data is a type of data that consists of categories that can be ordered or ranked. However, the distance between categories is not necessarily equal. Ordinal data is often used to measure subjective attributes or opinions, where there is a natural order to the responses. Examples of ordinal data include education level (Elementary, Middle, High School, College), job position (Manager, Supervisor, Employee), etc.

Ordinal data can be represented using bar charts, line charts. These displays show the order or ranking of the categories, but they do not imply that the distances between categories are equal.

Ordinal data is analyzed using non-parametric tests, which make no assumptions about the underlying distribution of the data. Common non-parametric tests for ordinal data include the Wilcoxon Signed-Rank test and Mann-Whitney U test.

Quantitative Data (Numerical Data)

Quantitavive Data is the type of the data that represents the numerical value of the data. They are also called the Numerical Data. This data type is used to represent the height, weight, length and other things of the data. Quantitative data is further classified into two categories that are,

• Discrete Data
• Continuous Data

Discrete Data

Discrite data type is a type of data in statistics that only uses Discrete Value or Single Values. These data types have values that can be easily counted as whole numbers. The example of the discreate data types are,

• Height of Students in a class
• Marks of the students in a class test
• Weight of different members of a family, etc.

Continuous Data

Continuous data is the type of the quantitative data that represent the data in a continuous range. The variable in the data set can have any value between the range of the data set. Examples of the continuous data types are,

• Temperature Range
• Salary range of Workers in a Factory, etc.

Difference between Quantitative and Qualitative Data

Quantitaive and Qualitative data has huge differences and the basic differences between them are studied in the table added below,

Quantitative data	Qualitative data
Data is depicted in numerical terms.	Data is not depicted in numerical terms.
Can be shown in numbers and variables like ratio, percentage, and more.	Could be about the behavioral attributes of a person, or thing.
Example: 100%, 1:3, 123	Examples: loud behavior, fair skin, soft quality, and more.

Difference between Discrete and Continuous Data

Discrete data and continuous data both come under Quantitaive data and the differences between them is studied in the table added below,

Discrete Data	Continuous Data
The type of data that has clear spaces between values is discrete data.	This information falls into a continuous series.
Discrete Data is Countable	Continuous Data is Measurable
There are distinct or different values in discrete data.	Every value within a range is included in continuous data.
Discrete Data is depicted using bar graphs	Continuous Data is depicted using histograms
Ungrouped frequency distribution of discrete data is performed against a single value.	Grouped distribution of continuous data tabulation frequencies is performed against a value group.

Read More,

• Mean
• Median
• Mode

Data Types in Statistics – FAQs

1. What is Data and its Types?

Data is a collection of raw facts and numbers that are processed to form any information and there are generally two types of data in statistics that are, Qualitative Data and Quantitative Data.

2. What are 4 Types of Data in Statistics?

The 4 types of data in statistics are,

• Nominal data
• Ordinal data
• Discrete data
• Continuous data

3. What are the Main Types of Data?

There are two main types of data in mathematics that are,

• Qualitative Data
• Quantitative Data

4. What is Discrete Data?

The data that has a particular value rather that the range of the value is called the discrete data.

5. What is Continuous Data?

The data that lies between the highest and the lowest value are called the continuous data. The range of the continuous data is the difference between the highest and the lowest data.

6. What is Primary Data?

Primary data in mathematics is defined as the data that is collected for the first time. It is pure data and no analysis is performed in this data.