# Explain different types of data in statistics

• Last Updated : 29 Oct, 2021

Data is defined as a systematic record corresponding to a specific quantity. Basically, data can be summarised as a set of facts and figures which can be used to serve a specific usage or purpose. For instance, data can be used as a survey or an analysis. Data in a systematic and organized form is referred to as information. In addition to this, the source of data primary or secondary is also an essential factor.

### Types of Data

There are the following types of known forms of data:

### Qualitative Data

Qualitative data is used to represent some characteristics or attributes of the data. The facts and figures depicted by the qualitative data cannot be computed. These properties reflect observable attributes. These are non-numerical in nature. The qualitative data characteristics are exploratory on a larger end than being conclusive in nature. For instance, data on attributes such as honesty, loyalty, wisdom, and creativity for a set of persons defined can be considered as qualitative data.

Examples:

• Attitudes of people to a political system.
• Music and art
• Intelligence
• Beauty of a person

Nominal Data

Nominal data is a sub-category belonging to one of the types of qualitative information. Also known as the nominal scale, it is used to label the variables without providing the numerical value for them. Nominal data attributes can’t either be ordered or measured. The nominal data can be both qualitative and quantitative in nature. For instance, some of the nominal data attributes are letters, symbols or gender, etc.

The examination of the nominal data is based on the usage of the grouping method. This method is based on the principle of the grouping of data into different categories. This is followed by the calculation of the frequency or the percentage of the data. The visualization of this data is done using the pie charts.

Examples:

• Gender (Women, Men)
• Eye color (Blue, Green, Brown)
• Hair color (Blonde, Brown, Brunette, Red, etc.)
• Marital status (Married, Single)
• Religion (Muslim, Hindu, Christian)

Ordinal Data

Ordinal data/variable is the specific type of data that follows a natural order.  The difference between the data values is not determined in the case of nominal data. For instance, ordinal data variable is mostly found in surveys, economics, questionnaires, and finance operations.

The examination of the nominal data is based on the usage of visualization tools. The visualization of this data is done using the bar chart. The ordinal data can be expressed in the form of tables which have each row corresponding to the distinct c

ory.

Examples:

• Feedback is recorded in the form of ratings from 1-10.
• Education level: elementary school, high school, college.
• Economic status: low, medium, and high.
• Letter grades: A, B, C, and etc.
• Customer level of satisfaction: very satisfied, satisfied, neutral, dissatisfied, very dissatisfied.

### Quantitative Data

Quantitative data can be measured and is not just observable. The measurement of data is numerically recorded and represented. Calculations and interpretations can then be performed on the obtained results. Numerical data is indicated by quantitative data. For instance, data can be recorded about how many users found a product satisfactory in terms of the collected rating, and therefore, an overall product review can be generated.

Examples:

• Daily temperature
• Price
• Weights
• Income

Discrete Data

Discrete data refers to the data values which can only attain certain specific values. Discrete data can’t attain a range of values. Discrete data can be represented using bar charts. For instance, ratings of a product made by the users can only be in discrete numbers.

Examples:

• The number of students in a class,
• The number of chips in a bag,
• The number of stars in the sky

Continuous Data

Continuous Data can contain values between a certain range that is within the highest and lowest values. The corresponding difference between the highest and lowest value of these intervals can be termed as the range of data. Continuous data can be tabulated in what is called a frequency distribution. The frequency distribution table can be computed for the range type of data. It can also be depicted using histograms. For example, the heights of the students in the class can be largely varying in nature, therefore, they can be divided into ranges to summarise the data.

Examples:

• Height and weight of a student,
• Daily temperature recordings of a place
• Wind speed measurement

### Sample Questions

Question 1. Difference between Quantitative data and Qualitative data?<

ong>

Solution:

Question 2. Difference between Discrete and Continuous Data?

Solution:

Question 3. Give any two examples of data collection.

Solution:

• Increase in population of our country in the last two decades.
• Number of rupees in the bag

Question 4. Illustrate:

A. Describe how was your overall experience using the product?

B. Describe how was your overall experience using the product?

• Good
• Poor

What type of data is illustrated by these points.

Solution:

A reflects nominal data whereas B reflects ordinal data.

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