Discrete vs. Continuous Data: A Guide for Beginners

Continuous and discrete data are foundational for many data-centric careers, ranging in focus from health sciences to finance and economics. By learning about the role each type of data plays and their distinctions from one another, you can make informed decisions about when to use each type of data, understand how it can guide decision-making, and explore how data shapes different types of professions. 

Keep reading to explore the definition of each data type, consider everyday examples, and hone in on essential differences between these data types.

What is data?

Data is a type of information that you can collect, organize, store, and analyze. It generally falls into two categories: qualitative and quantitative data. Qualitative data includes values that cannot be numerically measured, such as the color of a shirt, the responses to open-ended interview questions, or the cities in a country. 

Quantitative data, on the other hand, includes numerical values that can be counted or measured and analyzed statistically. In this article, we will focus specifically on quantitative data.

Read more: What Is Data Science? Definition, Examples, Jobs, and More

What is quantitative data?

Quantitative data is a type of data with a numerical value that you can analyze using statistical methods. When you collect quantitative data, you collect statistics or information in forms that can be measured or counted. You can examine and interpret this raw data to extract meaningful insights and guide business decisions. When you collect quantitative data, you can collect information in several forms, depending on your variable types. Generally, your variable type will fall into one of two categories: discrete or continuous measures. 

Discrete data

This data type can only take on specific, separated values and is typically counted rather than measured. Each discrete data point is distinct and separate from the next, with no possible data points in between.

Examples of discrete data

You can find discrete data throughout your daily life. Some examples of discrete data you may have come across include:

• The number of children in a classroom

• The number of pages in a book

• The number of cars in a parking lot

• The number of siblings you have

• The number of slices in a pizza

While you might assume discrete data must only include whole numbers, this is not the case. The key component of discrete data is a gap between adjacent values with no possible middle values. For example, a shoe size could be 8 or 8.5, but it could not be 8.25. 

Continuous data

Continuous data, in contrast, can take on any value within a defined range or interval. You measure these values rather than count them. This data type can include fractions or decimals, which allows for a nearly infinite number of potential values within a given range. For example, an individual's weight is a continuous data set, as it could be 150 pounds, 150.5 pounds, 150.55 pounds, and so forth.

Continuous data has two types of values: interval and ratio values. Interval data can have values below zero (such as a temperature reading), while ratio data has to be positive (such as weight).

Examples of continuous data

Like discrete data, you can find examples of continuous data in many aspects of daily life. Some ways you might see continuous data represented include:

• The temperature on a given day

• The height of individuals in a group

• The time it takes to run a race

These examples could include a virtually unlimited number of values within a specified range, represented as measurements rather than counts.

Differences between discrete and continuous data

While discrete and continuous data are both quantitative measures, discrete data is countable and distinct. Continuous data can take on any value within a defined range and is a measurable data type. This distinction leads to several differences in the type of information, visualization, and statistical analysis you can perform on the data values.

Statistical analysis

When analyzing variables independently, continuous variables allow you to look at descriptive characteristics of a data set, such as the mean, median, mode, range, and standard deviation of the data. Discrete variables have fixed values, so looking at frequency counts, proportions, and other general characteristics is more suited to the data than overall averages. For example, if you had a data set representing males by a “1” and females by a “2”, it would be more representative to look at the proportions of 1s and 2s than the mean of all values.

Sometimes, you may have discrete and continuous data and want to examine the association between two variable types. For example, you could look at the association between gender and height. In these cases, you would run a statistical analysis such as t-test, chi-squared, or logistic regression to look at statistical insights from the data.

Data visualization

Data visualization is essential to data analysis, providing a graphical representation of data to understand trends, patterns, and outliers better. You can choose to represent discrete data in the visualization, such as bar graphs, pie charts, or mosaic plots, where individual countable categories can be easily visualized. Continuous data, on the other hand, is best represented by histograms, line graphs, or frequency tables, which can effectively display a range of data points or a progression of data over time.

Read more: Data Visualization: Definition, Benefits, and Examples

Who uses continuous and discrete data?

Many real-world professions use discrete and continuous data. For instance, in the health care sector, both discrete and continuous data can be valuable. Discrete data, like the number of patients diagnosed with a specific disease, can guide public health initiatives. In contrast, continuous data, like a patient’s body temperature or blood pressure readings, can assist in individual treatment plans.

In the business sector, discrete data, such as the number of units sold, can inform inventory management, and continuous data, like time spent on a website, can fuel marketing strategies. Understanding these two data types allows professionals to make evidence-based decisions, improving outcomes and efficiencies.

Other examples of professions that rely on data to perform necessary operations and make decisions include:

• Data analyst

• Market research analyst

• Data architect

• Machine learning engineer

• Project manager

• Marketer

Next steps 

Continuous and discrete data are two commonly used data types across many professions, including health care, finance, and economics. Discrete data is countable data with distinct gaps between values, while continuous measures have infinite possible values between points. Understanding data types can help you choose the right statistical techniques, interpret data accurately, and make informed decisions. 

To learn more about data and data analysis techniques, consider completing courses or Professional Certificates on Coursera, such as the Google Data Analytics Professional Certificate, to build job-ready skills and explore how you can apply data in your field of interest.