Discrete vs. Continuous Data: What Is The Difference? (2024)

Learning the difference between discrete and continuous data and the use cases can seem overwhelming. However, data-driven insights are playing an important role in business success. The professionals who understand these unique data types can identify opportunities where data can come in handy. Marketing professionals can leverage this information to improve their strategies and optimize advertising campaigns.

What is numerical data?

Numerical data, also known as quantitative, is a data type expressed in numbers rather than natural language. Numerical data differentiates itself from other number form data types with its ability to carry out arithmetic operations with these numbers.

Quantitative data is split into two types of data: discrete one, which represents countable items. And continuous data, which outlines data measurement. The continuous numerical data is further subdivided into interval and ratio data, known for measuring certain items.

The discrete data fundamentals

Discrete data is a count that involves integers — only a limited number of values is possible. This type of data cannot be subdivided into different parts. Discrete data includes discrete variables that are finite, numeric, countable, and non-negative integers. In many cases, discrete data can be prefixed with “the number of”. For example:

  • The number of students who have attended the class;
  • The number of customers who have bought different products;
  • The number of groceries people are purchasing every day;

This data type is mainly used for simple statistical analysis because it’s easy to summarize and compute. In most of the practices, discrete data is displayed by bar graphs, stem-and-leaf-plot and pie charts.

Continuous data — it’s all about accuracy

Continuous data is considered the complete opposite of discrete data. It’s the type of numerical data that refers to the unspecified number of possible measurements between two presumed points.

The numbers of continuous data are not always clean and integers, as they are usually collected from very precise measurements. Measuring a particular subject is allowing for creating a defined range to collect more data.

Variables in continuous data sets often carry decimal points, with the number stretching out as far as possible. Typically, it changes over time. It can have completely different values at different time intervals, which might not always be whole numbers. Here are some examples:

  • The weather temperature;
  • The wind speed;
  • The weight of the kids;

Continuous data can be measured by using specific tools and displayed in line graphs, skews, histograms.

Discrete vs. continuous data — the comparison

Both data types are important for statistical analysis. However, some major differences need to be noted before drawing any conclusions or making decisions. The key differences are:

  • Discrete data is the type of data that has clear spaces between values. Continuous data is data that falls in a constant sequence.
  • Discrete data is countable while continuous — measurable.
  • To accurately represent discrete data, the bar graph is used. Histogram or line graphs are used to represent continuous data graphically. A diagram of the discrete function shows a distinct point that remains unconnected. While in a continuous function graph, the points are connected with an unbroken line.
  • Discrete data contains distinct or separate values. Continuous data includes any value within the preferred range.

The importance of discrete and continuous data

Both discrete and continuous data are valuable for all sorts of data-driven decisions and it's important to know how to interpret data. Valuable research and insights are made by combining both sets of data. Here are some examples where discrete and continuous data can be used:

  • Marketing and advertising. Before engaging in any marketing or advertising campaign, companies need to analyze internal and external factors that may affect the marketing campaigns. In most cases, marketing professionals are using SWOT analysis. A SWOT analysis is a compilation of the business’s strengths, weaknesses, opportunities and threats. The primary objective of this analysis is to help companies develop a full awareness of all the factors involved in making data-driven decisions. In that case, email marketing could be a great option through email marketing automation software.
  • Research. Numerical types of data are popular among researchers due to their compatibility with most statistical techniques. Discrete and continuous data helps to ease the research process.
  • Population analysis. Using trends analysis, researchers gather the data on various rates in a country or a region for a certain period and predict future populations. This might include birth, death rates, languages popularity, et cetera. Predicting a country's demographics plays a vital role in economics.
  • Product development. Product researchers use total unduplicated reach and frequency analysis (TURF) to investigate if a new product or service will have the demand and will be well-received in the target market during the product development stage.

However, the implementation of discrete or continuous data might not always provide accurate results, as there are challenges related to only analyzing numerical data. For example:

  • Discrete or continuous data research can be limited in their pursuit of statistical relationships. It can lead to researchers overlooking valuable insights. By focusing solely on numbers, analyst runs into the risk of missing big-picture information that can benefit the business.
  • When conducting research, analysts need to develop a hypothesis and set up a model for collecting and analyzing data. Any errors in the setup, bias on the analysts’ part, or execution mistakes can distort the results. Sometimes even coming up with a hypothesis can be subjective, especially if there is a specific question that needs to be answered and proved by not only numerical data.

How to collect and aggregate numerical data

As challenging as the discrete and continuous data might be, it’s the most useful type in statistical analysis. Numerical data allows businesses to make data-driven decisions and look for insights that help power up a business’s growth. The insights made from discrete and continuous data also enable marketers to measure their marketing efforts’ effectiveness and implement better strategies in the future. To protect user data online, people often use VPN malware to avoid tracking.

Whatagraph can come in handy and ease the labor-intensive process of data collection and aggregation. The reporting tool automatically gathers data from different sources and presents it in a visual report. The collected data can be shown in various charts and graphs, including pie charts for discrete data and line graphs for continuous data.

Bottom line

Hence, it’s quite clear that the two types of data are different in the explanations and examples. Discrete data presents a certain number of isolated values. In contrast — continuous data shows any value from a given range.

Understanding the numerical data and the difference between discrete and continuous data might pose a challenge initially. However, after mastering the data, marketing professionals will be able to support their performance insights with actual and accurate data.

Discrete vs. Continuous Data: What Is The Difference? (2024)


Discrete vs. Continuous Data: What Is The Difference? ›

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.

What is the difference between continuous and discrete data? ›

The key differences are: Discrete data is the type of data that has clear spaces between values. Continuous data is data that falls in a constant sequence. Discrete data is countable while continuous — measurable.

What is an example of discrete data? ›

The number of each type of treatment a salon needs to schedule for the week, the number of children attending a nursery each day or the profit a business makes each month are all examples of discrete data. This type of data is often represented using tally charts, bar charts or pie charts.

How do you identify whether a variable is discrete or continuous? ›

Step 1: Identify the variable in the problem. Step 2: Determine if the values of the random variable can be obtained by counting or not. If the variable can, it is discrete. If the variable cannot, it is continuous.

What is the difference between discrete and continuous data distribution? ›

A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite).

What is a continuous data example? ›

Examples of continuous data include weight, height, length, time, and temperature. Frequently, you'll use histograms and scatterplots to graph continuous data.

How do you know if data is discrete? ›

Data that can only take certain values is called discrete data or discrete values. This is data that can be counted and has a limited number of values. It usually comes in the form of whole numbers or integers.

What is an example of a continuous variable? ›

Therefore, at a macroscopic level, the mass, temperature, energy, speed, length, and so on are all examples of continuous variables. Another example of a continuous variable is height.

Is age discrete or continuous? ›

If you know a person's time of birth, you could measure their age precisely up to the second or even millisecond if you wanted to. In this sense, age is a continuous variable. However, we don't usually care about a person's exact age. Instead, we treat age as a discrete variable and count age in years.

What is the difference between discrete and continuous data quizlet? ›

Continuous data is connected. Is discrete data connected or distinct? Discrete data is distinct and separate.

Is time discrete or continuous? ›

Between any two points in time there are an infinite number of other points in time. The variable "time" ranges over the entire real number line, or depending on the context, over some subset of it such as the non-negative reals. Thus time is viewed as a continuous variable.

Is discrete data qualitative or quantitative? ›

Discrete data are a type of quantitative data that can take only fixed values. They are always numerical. These are data that can be counted, but not measured.


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