Understanding Different Types of Data in Statistics

Familiarity with data types is crucial not just for data scientists but also for business owners, marketers, or other professionals who deal with structured or unstructured data in various industries, including banking, agriculture, real estate, and health care. The right knowledge of data types enables precise metrics evaluation, sound conclusions, appropriate analysis, interpretation, and presentation. 

Use this guide to get insights into the different data types in statistics to help you utilise them better. 

Types of data 

Data is broadly classified into qualitative and quantitative, which are further divided into sub-categories. Qualitative data comprises nominal and ordinal data, whereas quantitative data consists of discrete and continuous data.

Quantitative data

You can measure quantitative data. You can numerically record and represent this data measurement. Moreover, it serves as a foundation for calculations and interpretations. 

With access to quantitative data, you can answer questions like “how many, "“how much,” and “how often.” 

For instance, quantitative data of the ratings collected from consumers who found the software satisfactory helps generate an overall product review. 

Quantitative data can easily be represented using bar graphs, scatter plots, charts, and other statistical methods.

Common examples of quantitative data:

• Income of individuals

• Daily temperature

• Test scores

• Price of items

• Number of hours of study

• Weight of a person

Two types of quantitative data in statistics are discrete data and continuous data.

Discrete data

Discrete data refers to data values that can only attain specific values and not a range of values. In other words, discrete data involves only integers. It must be divided into parts.

Families come in all shapes and sizes. The number of members per family can be classified as discrete data, meaning it is counted rather than measured. You will count individuals, and you cannot have answers like 1.5.  

You can represent discrete data using bar charts, column charts, spider charts, stacked bar charts, and stacked column charts. 

Common examples of discrete data:

• The number of participants in an event

• The number of students in a school

• The number of questions in an exam

• The number of employees in a company

• The number of chairs in a room

• The number of biscuits in a packet

Continuous data 

Continuous data is quantitative data that describes data points that are not separated by distinct intervals. It can include values between a specific range and be further divided into parts. Its values typically lie within the highest and lowest values. Continuous data changes over time, may or may not be whole numbers, and is measured using line graphs, skews, and other data analysis methods.

For instance, the weight of students in a class can vary. Therefore, dividing the data into ranges helps in effective evaluations. Between 60 and 70 kilograms, there are thousands of possible weights, such as 62.04762 kilograms, 69.948376 kilograms, and so on.

Discrete data can only take certain values, while continuous data can take any value within a given range. You can tabulate continuous variables using a frequency distribution table.

Common examples of continuous data:

• Height of a student

• Temperature recordings of a place

• Speed of a car or bike

• Daily wind speed

• Length of customer service calls

• Time required to complete a task

Qualitative data

Qualitative data represents an attribute or characteristic. You cannot compute the facts and figures represented by qualitative data, which reflects observable properties with a non-numerical nature. Its attributes are exploratory instead of conclusive. 

Qualitative data is also termed categorical data because it can be sorted into categories rather than numbers. It answers important questions like “how things happened” or “why they happened.”

For example, data on characteristics like loyalty, truthfulness, creativity, and others are qualitative data. 

Common examples of qualitative data:

• The beauty of a person

• Intelligence

• Favourite holiday destination

• Ethnicity

• Birth cities

• Colours of the land

Nominal data and ordinal data are two types of qualitative data.

Nominal data

Nominal data is a type of qualitative data that symbolises variables without assigning them numeric values. You cannot measure nominal data attributes, but you can examine nominal variables using the grouping method. Pie charts are typically used to visualise this type of data.

Common examples of nominal data:

• Marital status (single, married, widowed, divorced)

• Gender (women, men)

• Eye colour (black, green, brown, blue)

• Hair colour (blonde, red, brunette, black)

• Names of people

• Nationalities

Ordinal data

Ordinal data is a specific type of data that shows a number in order. Since it only reflects a sequence, you cannot perform arithmetic calculations on ordinal data. 

The data are arranged in a sequence according to their values on a scale, which can indicate superiority or inferiority.

Ordinal data can be examined using visualisation tools such as a bar chart or tables in which every row corresponds to a distinct category.

Common examples of ordinal data:

• Education level (high school, BS, MS, PhD)

• Letter grades (A, B, C, D and more)

• Satisfaction level of a customer (Very satisfied, satisfied, dissatisfied)

• Several positions in a competition (First, second, third, and so on)

• Socioeconomic status (poor, low income, middle income, upper-middle income, or high income)

• Language proficiency (beginner, intermediate, fluent)

Learn more about data with Coursera.

The different types of data used hold substantial significance in various aspects of statistics, data science, and research. A clearer understanding of the different data types can propel your knowledge further. 

Learn more about data science and discover the possibilities that come with this ever-evolving field. Learn more about its potential and how to use it by enrolling in Google's Foundations: Data, Data, Everywhere course, completing an Introduction to Data Science Specialisation, or earning an IBM Data Science Professional Certificate to expand your skill set. 

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