by 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 details, what are the main differences?
Read on for explanations and examples of how both types of data help people interpret numerical data, as well as some things that separate discrete and continuous data.
What is Discrete Data?
Discrete data is a type of data that is made up of separate distinct values or categories, which means that you cannot break discrete values down into smaller parts.
Think of it this way: Discrete data points represent countable items, making them better suited for situations where precise counting or categorization is important.
Examples of Discrete Data
Here are some examples of when you could use discrete values, or whole numbers:
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Number of students in a classroom: The number of students in a classroom is a discrete data point because you cannot count a fraction of a student. The figure will always be an integer.
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A roll of the dice: When you roll a standard six-sided die, the possible outcomes are discrete numbers from one to six, making dice rolls discrete data rather than continuous data.
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Shoe sizes: When shoe manufacturers release their products in full sizes such as 5, 6, 7, and so on, you can consider these to be isolated data.
What is Persistent Data?
Continuous data represents a range of values that you can measure precisely. This results in any value within a given range, including fractions and decimals.
Since it is ideal for measuring quantities that may change indefinitely, continuous data is best used when you need to be very precise.
Examples of Continuous Data
Continuous data figures include fractions or decimal values. Here are a few examples:
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Height: People rarely round their height to the nearest foot or metre. Individual heights can vary over a range, and measurements usually show this as continuous data – for example, a height of 5.7 feet or a height of 150.2 centimeters.
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Weight: Like people’s heights, weight measurements can also be continuous, such as 150.5 pounds or 68.3 kilograms.
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Temperature: Temperature readings, such as 32.5 C, 20.1 C, or 98.6 F, are continuous data because they can take any value within a range.
Discrete Data vs. Continued: 4 Main Differences
To understand the basics of discrete and continuous data, you need to know the key differences between them.
1. Discrete Data are Whole Numbers, While 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 you collect and analyze data, you will need to decide how precise you need the figures to be. That will determine what type of data you use.
2. Continuous Data is More Accurate.
Since continuous data allows for fractions or decimals, it allows you to measure something down to a very specific figure. Discrete data, on the other hand, provides less precision because it only deals with whole numbers or specific categories.
3. Visualization of Discrete Data Could Use a Bar Graph, While Continuous Data Could Use a Line Graph.
To represent discrete data, people often use bar graphs, histograms, or other methods that show the frequency of different categories or values. In contrast, people use line graphs to represent continuous data and to show how the data points change continuously over a certain range.
4. Scientific Research is Likely to Use Continuous Data.
Fields that require very precise measurements, such as engineering, medicine and quality control, gravitate towards continuous data and the detailed information that continuous data provides.
However, in areas where counting and categorization are important, people rely more on discrete data to work on things like inventory management, demographics or survey responses.
Can You Treat Continuous Variables as Discrete Variables?
You may 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, for example in survey analysis.
To further illustrate this example, imagine that you are conducting a survey to study how people in different age groups prefer to go to work. In addition to the modes of transportation, you collect data on the ages of the respondents, which is a continuous variable because 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 and so on).
This may make the analysis more manageable and the results clearer, especially if you want to perform statistical tests comparing the preferences of the age groups.
This article was created in collaboration with AI technology, then fact-checked and edited by a HowStuffWorks editor.
Original article: Understanding Discrete vs. Continuous Data and Uses for Everyone
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