In the world of data analysis and statistics, continuous and discrete data play fundamental roles. These two types of quantitative data serve different purposes as people use them to draw valuable insights and make informed decisions.
But when it comes to discrete vs. continuous data, what exactly are the key differences?
Read on for explanations and examples of how both data types help people interpret numerical data, as well as some things that set discrete and continuous data apart.
What Is Discrete Data?
Discrete data is a type of data that consists of distinct, separate values or categories, meaning you can't break discrete values down into smaller parts.
Think of it like this: Discrete data points represent countable items, making them best suited for situations where precise counting or categorization is important.
Examples of Discrete Data
Here are some examples of when you might use discrete values, or whole numbers:
The number of students in a classroom: The number of students in a classroom is a discrete data point since you can't count a fraction of a student. The figure will always be a whole number.
The roll of a die: When you roll a standard six-sided die, the possible outcomes are distinct numbers from one to six, making die rolls discrete data rather than continuous data.
Shoe sizes: When shoe manufacturers release their products in whole-number sizes like 5, 6, 7, and so on, you can consider this to be discrete data.
What Is Continuous Data?
Continuous data represents a range of values that you can measure with precision. This results in any value within a given range, including fractions and decimals.
Since it's ideal for measuring quantities that can vary indefinitely, it's best to use continuous data when you need to be very precise.
Examples of Continuous Data
Continuous data figures include fractions or decimal values. Here are a few examples:
Height: People seldom round their height to the nearest foot or meter. The height of individuals can vary across a range, and measurements usually reflect this as continuous data — for example, a height of 5.7 feet or a height of 150.2 centimeters.
Weight: Similar to people's height, weight measurements can also be continuous, such as 150.5 pounds or 68.3 kilograms.
Temperature: Temperature readings, like 32.5 C, 20.1 C, or 98.6 F, are continuous data because they can take on any value within a range.
Discrete vs. Continuous Data: 4 Key Differences
To understand the basics of discrete and continuous data, it's necessary to be familiar with the main differences between them.
1. Discrete Data Are Whole Numbers, Whereas Continuous Data Can Be Fractions or Decimals.
Discrete data points are distinct, separate and countable, while continuous data points are part of a continuous spectrum. Before collecting and analyzing data, you will have to determine how precise you need the figures to be. That will determine which type of data you use.
2. Continuous Data Is More Precise.
Since continuous data allows for fractions or decimals, it enables you to measure something down to a very specific figure. Discrete data, on the other hand, provides less precision since it deals only with whole numbers or distinct categories.
3. Discrete Data Visualization Might Use a Bar Graph, Whereas Continuous Data Might Use a Line Graph.
To represent discrete data, people often use bar graphs, histograms or other methods that will show the frequency of the different categories or values. By comparison, people use line graphs to represent continuous data and show how the data points change continuously over a given range.
4. Scientific Research Is More Likely To Use Continuous Data.
Fields that require very precise measurements, such as engineering, medicine and quality control, gravitate toward continuous data and the detailed information that continuous data provides.
In fields where counting and categorization are important, however, people rely more on discrete data for work on things like inventory management, demographics or survey responses.
Can You Treat Continuous Variables as Discrete Variables?
It's possible you might treat continuous variables as discrete variables under certain conditions. For example, you might treat a continuous variable as a discrete variable in the context of age groups or age categories, such as in a survey analysis.
To illustrate this example further, imagine you are conducting a survey to study how individuals in different age groups prefer to get to work. In addition to the modes of transportation, you collect data on the respondents' ages, which is a continuous variable since age can take any value within a range.
However, for the purposes of your analysis and interpretation, you may choose to treat age as a discrete variable by categorizing respondents into age groups (age 18 to 24, age 25 to 34, age 35 to 44 and so on).
This can make the analysis more manageable and the results more interpretable, particularly if you want to perform statistical tests comparing the preferences of the age groups.
This article was created in conjunction with AI technology, then was fact-checked and edited by a HowStuffWorks editor.
Original article: Understanding Discrete vs. Continuous Data and Uses for Each
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