What Is Discrete Data vs. Continuous Data? Uses and Examples

Updated October 13, 2023

An illustration of a person gesturing to a sampling distribution graph.

Quantitative data, or data you can measure, is information many businesses review when assessing the success of individual products or departments. Companies can measure many types of quantitative data, including discrete and continuous data. Understanding these types of data and their differences can help you collect and analyze information accurately to help businesses make decisions.

In this article, we define discrete and continuous data, provide some examples of each, discuss their differences and share ways to measure and represent this data effectively.

What is discrete data?

Discrete data is a type of quantitative data that includes nondivisible figures and statistics you can count. You typically write discrete data points as numbers that represent exact values. You can often describe discrete data by using the phrase "the number of," such as the number of customers in a store.

Discrete data usually represents single events that have already occurred. When reviewing discrete data, you can analyze exact figures like units sold on a specific day or the hours an employee worked during a certain week.

Examples of discrete data

Businesses use discrete data to track a wide variety of relevant information. Here are some examples of discrete data you might track for a company:

Ticket sales

The number of ticket sales on one day is a common example of discrete data. Knowing the number of tickets sold can allow a business to prepare for the correct number of attendees or visitors. Ticket sales are discrete data because the number of sales doesn't change after the company stops selling tickets.

Number of employees

The number of employees a company has is another type of discrete data. Companies may track their number of employees because this information is relevant to their growth goals. Some companies also try to maintain a specific ratio of management to lower-level employees to ensure every employee receives guidance and direction in their roles.

Number of product reviews

The number of reviews a company's product receives in a specific time frame, such as one week, is another example of discrete data. Companies that wish to track customer responses to their products may offer consumers an option to leave reviews. Tracking the number of reviews a product receives can be useful when analyzing customer satisfaction.

Employee hire dates

Many businesses track their employees' hire dates. Often, companies start the onboarding process with a training process that takes a few days or weeks. Recording the amount of time an employee has worked with a company allows managers to know their current stage of training. An employee's hire date may also be relevant for knowing when their employee benefits, such as health insurance, become active.

What is continuous data?

Continuous data is a type of quantitative data that represents precise measurements of nearly any numeric value. Often, a continuous data variable has many decimal points because it's an exact measurement between two defined points.

Measuring this type of data is common in industries requiring specific data, such as health care, manufacturing and research and development. Continuous data may change over time, allowing businesses to analyze their operations and predict future trends. For example, a business may track the amount of time it takes a team to complete projects.

Examples of continuous data

In addition to discrete data, companies also use continuous data to gain insights into their sales, operations and growth opportunities. Here are some examples of continuous data in business:

Sales in one year

A company's sales in a defined long-term period, such as one year, is an example of continuous data. This type of continuous data allows businesses to see trends in sales throughout the year, such as a rise in deals during the winter months. A company's sales figures can change over time, making it an example of continuous data, rather than discrete data.

Weight of product boxes

Many manufacturing companies use continuous data to measure the weight of product boxes. They use scales to weigh a product box for precise measurements so they can list its weight accurately. A company may also check the weight of a product box to ensure it matches the number of products inside, which is an example of using continuous and discrete data simultaneously.

Customer service calls

A business may track the length of customer service calls as a type of continuous data. Companies may use this information to understand how much time it takes customer service representatives to assist customers with their questions or concerns. Often, companies consider the average length of customer calls to determine whether representatives are using their time efficiently.

Time spent on a website

Many companies track the amount of time visitors spend on their websites. They can measure this time in precise increments to determine how much time visitors spend engaging with content on the site. A business may also use this metric to optimize its website to allow users to navigate it quickly.

Discrete data vs. continuous data

Discrete data and continuous data are both types of quantitative data. The main difference between them is the type of information they represent. Discrete data typically only shows information for a particular event, while continuous data often shows trends in data over time. Some other differences between discrete data and continuous data include:

Values: Discrete data represents exact figures you can count, such as the numbers of students in a class. In contrast, continuous data often includes measurable values representing a range of information, such as the extent of the difference between the shortest and tallest student in a class.
Types of data: Discrete data typically has an integer value, which is a whole number. Continuous data values often have fractions or decimal points.
Methods: You can usually measure discrete data using simple methods, such as a number line or bar chart. You may use more complex methods to represent continuous data, such as curves or histograms.
Time intervals: Typically, discrete data remains constant over a specific time interval, whereas continuous data has different values at various intervals over time.

Discrete/continuous data visualization

Visualizing discrete and continuous data can be an important step to help you understand the performance of a business. Here are a few ways you can represent this type of data:

Bar graphs

You can use a variety of methods to represent quantitative data visually, but one of the most common approaches is to use a graph. A bar graph, in particular, is a typical way that businesses chart discrete data.

Bar graphs can show individual data points for a few categories, such as the number of sales of five different employees in May. Bar graphs are useful when plotting discrete data because they allow you to compare the differences between individual data points visually.

Histogram

Similar to a bar graph, a histogram displays data as a series of bars, but it often groups data into a specific range of values. It's common to use a histogram to display trends in continuous data. For example, a histogram may show the average time a customer waits to speak with a representative at a call center. Each bar on the histogram may depict a single time range, such as 30 to 60 seconds.

Frequency tables

A frequency table is a simple chart that typically has a category side and a numeric value side. For example, a company might label a frequency table, "Menu items sold today" with the name of the item on one side and the number of items sold on the other side. Frequency tables can make it easier to visualize trends in popular items.

Plotted points

A plotted points graph is a simple graph with an x-axis and a y-axis. On this graph, you can plot discrete or continuous data freely to show data trends with a limited or high number of variables. This type of graph allows you to view data quickly, which can be beneficial when trying to understand general trends.