Sometimes we create data without even realizing it — sending a text message, posting a photo on Instagram or just browsing through various websites. To put a number on it, in 2020, people generated 2.5 quintillions of data every second. Like the many ways to create data, there are plenty of various data types. There are structured and unstructured data. Then there is qualitative and quantitative data. And finally, there are discrete vs. continuous data, which is the fundamentals for every person working with businesses.
Jul 29, 2021 ● 7 min read
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.
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.
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:
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 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:
Continuous data can be measured by using specific tools and displayed in line graphs, skews, histograms.
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:
Both discrete and continuous data are valuable for all sorts of data-driven decisions. Valuable research and insights are made by combining both sets of data. Here are some examples where discrete and continuous data can be used:
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:
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.
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.
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.
Published on Jul 29, 2021
WRITTEN BY
Benediktas KazlauskasBenediktas is a detail-oriented writer with a passion for marketing and technologies. Most of the time, you can find him holding a cup of coffee and crafting another data-backed, insights-packed content piece.
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