The difference between discrete and continuous data

You'd be hard-pressed to find any business, research, or marketing model that doesn't benefit from the collection and analysis of data. But not all metrics are measured or assigned a value in the same way. There's data, and then there are discrete and continuous data.

Today's researchers, businesses, and marketers are tapping into new ways to leverage data and metrics, including both discrete and continuous data. To better understand how either can improve your processes and outcomes, we'll dive deeper into the difference between discrete and continuous data.

This is all about how you can track progress and growth and spot opportunities for improvement in whatever you're measuring. Once you understand the unique differentiators, benefits, and applications, you can start to effect improvements in your processes and results.

When marketers and researchers talk about discrete data, they're referring to a numerical type of data that involves concrete and whole numbers – not fractions or decimals. These numbers represent fixed data values and are determined and calculated by counting in a linear way. If it's whole, positive, and countable, it's representative of discrete data.

Examples of discrete data

Discrete data can be qualitative or quantitative and are best described with examples. Examples of discrete data include:

• The number of children in a family

• The number of pets in a household

• The number of students in a classroom

Depending on your project or business model, you can use these samples to help you decide what discrete data points are evident in your calculations. Remember, you're looking to spot data that can be counted linearly with positive integers.

Let’s look at some more examples of discrete data.

Ticket sales

Any business, industry, or event that relies on ticket sales deals with discrete data. These calculations are whole, non-negative numbers that occur sequentially. What’s more, the number of sales can be counted, making ticket sales an example of discrete data.

Number of employees

Another example of discrete data is company growth in terms of employee numbers. Your analytics might seek to capture growth and scaling results. The discrete data related to the number of employees added, over time or in general, will be your insights.

Number of product reviews

Using another business example, one of your metrics might include analyzing product ratings about your core offerings. Those data points you collect that calculate the number of product ratings over time or for a particular product are considered discrete data.

Key characteristics of discrete data

Remember, discrete data relates to countable numbers in an individual counting model. There are certain characteristics of discrete data to help you differentiate them from continuous data or other metrics.

If your captured variables present these key formats or characteristics, you're working with discrete data:

• Finite

• Numeric and countable

• Non-negative integers

• Can be categorical (divided into groups)

• Distributed discretely relating to time and space

In contrast to discrete data, continuous data is defined as complex, often varying in value, and measured over time. These values will fluctuate over captured periods.

Examples of continuous data

Continuous data can be measured on a scale, such as temperature or height. Whether you work in marketing and sales or research and product development, you might be surprised how often you rely on continuous data within your analytics environment. Explore these continuous data examples to see which resonates most with your projects and tasks.

Time spent on a website

For digital designers and marketers, time spent on a project will contribute to the ROI of that project. For example, when you calculate the time it has taken to design or develop a website, you can use that continuous data to structure billing.

Not using your continuous data could mean missing the mark in your statements of work (SOW) and invoicing.

Sales in one year

Anyone in a management, sales, or leadership role will often review year-end data and metrics to gauge growth and areas of improvement. One of the most common examples of continuous data is year-end sales.

The analytics involved in calculating those accumulated conversions over time is continuous data.

Customer service calls

Service-based businesses will often measure their success in terms of measurable service calls. So, if your situation calls for a tabulation of customer service calls initiated over a certain period, you're collecting and measuring continuous data.

These datasets might fluctuate over time and can be calculated using more complex methods.

Key characteristics of continuous data

With those continuous data examples in mind, you can also explore these common characteristics to identify other continuous data options in your model. Remember, these metrics refer to changes in concepts over space and linear time. And unlike discrete data, continuous data requires measurements and is not simply counted. If your captured data has these characteristics, you're evaluating continuous data.

• Variables change with time

• Variables have different values at any given interval

• Variables are random and may or may not be whole numbers

• Variables are measured using line graphs and skews

• Variables are used in regression analysis

Visualization elements are key when sifting and analyzing any kind of data. There are various charts and graphs you can use for representing both discrete and continuous data collections. 

Bar graphs

Most often used in visualizing discrete data, the bar graph will group data into rectangular bars with proportionate lengths to represent counted variables.

Histogram

The histogram is a visualization tool to demonstrate averages. For this reason, histograms are better tools for representing continuous data, which is more complex, fluctuating, and time relevant. If you have too many discrete data values to show effectively on a traditional chart, you can use a histogram.

Frequency tables

A frequency distribution table is a helpful graph when discrete data values are small. It's one of the most standard chart designs for discrete data visualization. However, you can also use a grouped frequency table to highlight continuous data.

Plotted points

Plotted point visuals are commonly used for demonstrating continuous data. Scatter plots, for example, can show relationships between continuous variables like x and y.

Box and whisker plots are also helpful in visualizing the distribution of continuous variables. Alternatively, stem plot graphs are great for discrete data descriptions with their proportional values.

Data collection and analytics are pivotal to success in today's business, design, marketing, and research environments. Knowing what kind of data you have can help you improve your methods of analyzing and applying that data.

Look for instances of discrete and continuous data in your ecosystem and be mindful of the differences, applications, and benefits of each. Collecting and visualizing those findings will enhance your ability to achieve growth and success in any endeavor.

Discrete values are essentially discrete data representing counting in a linear form as whole numbers. Discrete values are finite numbers counted in a captive period.

Age is an example of a continuous variable because it can be calculated in different integers, including years, months, days, hours, and minutes. Those measurements could be percentages with decimal points, although they can typically be rounded to a whole number for graphing purposes.

In some circles, age is also considered a discrete variable because it can be counted linearly and represented in a whole number of “years old.”

Remember, those data points that can be counted linearly and represent whole, non-negative numbers are discrete values. Any countable data you collect to calculate value is discrete.

The data is continuous if the data points are open to any possible number of values, negative or positive and whole or fractional.

A discrete variable assumes a distinct, countable value. Look for variables that can be counted to represent a value. The discrete values are limited to the amount and value of change in positive integers. As an example, you can always count the coins in your pocket.

The continuous variable takes on a set of values that cannot be counted or that has an infinite timeline. Any instances of data that can be variable on an unlimited number of values will be continuous. Think of measuring speed or distance as an example.