What Is Discrete and Continuous Data? Definition and Examples

By Indeed Editorial Team

Updated August 14, 2022

Published November 30, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

When working with data, it's important to know the different types. Data can be descriptive, such as short and tall, or numerical, and both types can be discrete or continuous. Understanding discrete and continuous data can help you analyze and interpret your data more accurately. In this article, we answer the question, "What is discrete data and continuous data?" provide characteristics and examples of each, and discuss their benefits and drawbacks.

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What is discrete data?

Discrete data is quantitative information with limited values. For example, if you were analyzing the number of customers you have each month, you could have 1 to 1 million customers, but not 10.5 or 578.9 customers. This means, in this scenario, you're limited to data with whole numbers as you can't divide people into smaller parts.

We can further divide discrete data into nominal or ordinal. Ordinal data is typically numerical as it is information that has a specific order or rank, such as first, second, and third place. Nominal data has no order as it fits into one or more categories, such as the different age groups of your customers.

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Characteristics of discrete data

To determine whether the data you're looking at is continuous or discrete, consider the following common characteristics of discrete data:

  • Includes discrete variables that are non-negative integers, countable, numeric, and finite.

  • Easy to visualize as you can easily create a bar chart, pie chart, tally chart, or line chart with discrete data.

  • You can easily count discrete data when it's numerical as it's usually whole numbers.

  • You cannot measure discrete data. For example, you can measure your height and weight, so it's not discrete data.

  • If you aren't sure whether your data is discrete, try to put "the number of," in front of it. For example, "The number of customers who purchased from us last month," or, "The number of employees the company has."

Essentially, the best way to determine whether your data is discrete is to answer the two following questions: can I count it and can I divide it into smaller parts? Your answer to the first question should be yes, and your answer to the second question should be no. For example, if your data is about the number of customers that purchased a service from you last month, you can count them, but you can't divide them into smaller parts.

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Examples of discrete data

To further help you understand discrete data, consider the following examples:

  • the number of students in a high school

  • the number of employees in a department

  • the number of customers that came in last month

  • the number of products on your shelves

  • the number of groceries customers are purchasing each day

  • the number of materials that were damaged during shipping

  • the number of computers each department has

  • the number of managers at the company

  • the number of languages each employee speaks

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Benefits of discrete data

Here are some of the benefits of using and analyzing discrete data:

  • You don't need large amounts of discrete data to make inferences or valid analyses.

  • It's often easy and cost-effective to gather small samples of discrete data.

  • There are a lot of ways to analyze discrete data, such as scatter plots, bar graphs, or tallies.

  • You can extrapolate or interpolate discrete data. This means you can forecast the future or estimate unknown variables using the data you already have.

  • It's easier to read and analyze discrete data as it appears more organized in graphs and plots.

Drawbacks of discrete data

Consider the following drawbacks of discrete data when determining whether you should analyze or interpret it at work:

  • Discrete data isn't as detailed as continuous data, so you can't gain as much insight.

  • You can't divide discrete data into smaller pieces, making it harder to analyze.

  • Discrete data may not be as precise as continuous data.

What is continuous data?

To understand discrete data better, you may need to learn about continuous data as well, since it is the opposite of discrete data. Continuous data can include any value on an infinite scale. For example, if you work in a restaurant, you may need to note the temperature of the fridge every day, which is continuous data as the temperature can be any number.

This means you can divide discrete data into smaller numbers with a new meaning. This often leads to data with many decimal points, so it's not as clean as discrete data. For example, if you're measuring how long it takes you to bake a cake at work, you can time yourself down to the exact second. Your results could be 1 hour, 15 minutes, and 23 seconds.

Characteristics of continuous data

Here are some of the characteristics of continuous data:

  • Can change over time, such as a person's height.

  • Made of random variables and different values.

  • Measured using skews, line graphs, histograms, or curves.

  • Harder to count, but easy to measure.

  • You can subdivide the data into smaller pieces with new meanings.

  • Typically measured on an interval scale or a ratio scale.

  • Can include an infinite amount of values.

Examples of continuous data

Consider the following examples of continuous data to help you understand the concept better:

  • the weather temperature

  • the wind speed

  • a person or object's weight

  • the temperature of a fridge or freezer

  • the height of a person or object

  • the amount of time it takes to complete a process, such as manufacturing a product

  • the speed of a vehicle

  • the square footage of an area

  • the amount of rain that fell

Benefits of continuous data

To help you decide which type of data you want to use, consider the following benefits of continuous data:

  • More accurate and detailed than discrete data as it can use decimal points and include infinite values.

  • Expresses variation in results better than discrete data does.

  • There are a variety of analysis options to look at continuous data, such as histograms and line graphs.

  • You don't need a lot of continuous data when using graphic analysis and statistical tests.

Drawbacks of continuous data

It's important to consider the drawbacks of continuous data as well, such as the following:

  • Some measurement tools can be restrictive. For example, a scale may show that an object is 55 pounds when it is actually 55.225 pounds.

  • Continuous data can be confusing to work with as numbers can be long with decimal points.

  • Collecting continuous data can be more expensive as it often takes longer.

Should you use discrete data or continuous data?

Although you know about both types of data now, you may still be unsure about which one to use. The answer may depend on the type of result you're looking for, but you don't actually need to choose between discrete or continuous data. Both types are important for statistical analysis. Collecting and analyzing both types of data can help form well-rounded results and research.

If you're trying to determine whether you're working with discrete or continuous data, ask yourself the following questions:

  • Can you reduce the measurement in half and still have it make sense? If the answer is yes, it's continuous data. For example, if you're analyzing the measurements of a product and the results are 53.89 inches and 98.541 inches, you can cut them in half and still have a number that represents a measurement.

  • Can you count the data? If the answer is yes, it's probably discrete data as it's usually whole numbers. For example, you can count how many employees you have but you can't count their height.

  • Can you measure the data? If the answer is yes, it's continuous data. For example, you can measure the amount of time customers spend in the store but not the number of customers that enters it.

  • Are there clear spaces between values? If the answer is yes, it's discrete data. For example, there's a clear space between 1, 2, 3, and 4, but continuous data could have numbers like 1, 1.5, 2, 2.4, 3, and 7.8, with no clear space between values.

  • What type of graph is the data in? Continuous data typically appears in a histogram, box plot, control chart, or scatter diagram. Discrete data typically appears in a bar chart, pie chart, tally chart, or line chart.

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