Discrete Data vs. Continuous Data: What's the Difference?
Edited by Janet White || By Harlon Moss || Updated on October 17, 2023
Discrete data has distinct, separate values (e.g., number of students); continuous data can take any value within a range (e.g., height).
Key Differences
Discrete data and continuous data represent two fundamental types of quantitative data in statistics. Discrete data is characterized by distinct, separate values that can be counted. For instance, the number of books in a library or the number of employees in a company falls under discrete data.
On the other hand, continuous data can assume any value within a specified range. It often deals with measurements rather than counts. For instance, measuring the weight of an individual or the temperature of a room are instances of continuous data.
While discrete data can only take on a finite number of values, continuous data can potentially take on an infinite number of values. If you think of a number line, discrete data points would be specific points on that line, whereas continuous data could be any point along that line within a certain range.
In the real world, continuous data is often rounded or approximated because it's impossible to measure with perfect accuracy. For instance, while a person's height is continuous, we might record it to the nearest centimeter or inch. In contrast, discrete data does not require such rounding because it represents whole, countable items.
When graphing these data types, discrete data might be represented using bar graphs, while continuous data is typically shown with histograms or line graphs.
ADVERTISEMENT
Comparison Chart
Type of Values
Distinct, separate values.
Any value within a specified range.
Example
Number of students in a class.
Height of students.
Representation
Specific points on a number line.
Any point along a number line within range.
Real-world Usage
Counts (e.g., items, people).
Measurements (e.g., weight, temperature).
Graph Representation
Bar graphs.
Histograms or line graphs.
ADVERTISEMENT
Discrete Data and Continuous Data Definitions
Discrete Data
Data that can only take certain, distinct values.
The number of pets in each household represents discrete data.
Continuous Data
Quantitative data that can be measured.
The height of a mountain is an example of continuous data.
Discrete Data
Data that isn't continuous.
The results of a dice roll provide discrete data.
Continuous Data
Data without distinct, separate values.
The time it takes for a train to complete a journey is continuous data.
Discrete Data
Values that are distinct and separate.
The shoe sizes sold in a store are discrete data.
Continuous Data
Infinite possible values within a specified range.
The weights of babies at birth provide continuous data.
Discrete Data
Data that has specific possible outcomes.
The number of goals scored in a soccer match is discrete data.
Continuous Data
Data that can assume any value in a given range.
The amount of rainfall in a city represents continuous data.
Discrete Data
Quantitative data that can be counted.
The number of cars in a parking lot is an example of discrete data.
Continuous Data
Data that flows and doesn't have gaps or jumps.
The speed of a car on a highway represents continuous data.
FAQs
Can the number of pages in a book be considered discrete data?
Yes, because the number of pages is a countable, distinct value.
What is a key characteristic of discrete data?
Discrete data consists of distinct, separate values.
Is a person's age an example of continuous data?
While age can be considered continuous, it's often treated as discrete when measured in whole years.
How is continuous data typically represented?
Continuous data is often shown using histograms or line graphs.
Can discrete data take decimal values?
Yes, as long as the values are distinct and countable.
Is temperature an example of continuous data?
Yes, because temperature can take any value within a range.
How are discrete data points represented on a graph?
Discrete data points are often represented using individual bars or dots.
Are percentages examples of discrete or continuous data?
While they can vary continuously, percentages are often treated as discrete when they represent distinct, countable categories.
What kind of data is the weight of a bag of apples?
It's continuous data, as the weight can be any value within a range.
Can continuous data be converted to discrete data?
Yes, continuous data can be binned or rounded to be treated as discrete.
Is the number of students in a classroom discrete or continuous?
It's discrete data because it's a countable number.
Is blood pressure an example of continuous data?
Yes, because blood pressure can vary within a range.
How is discrete data different from categorical data?
While both are non-continuous, discrete data is quantitative, and categorical data is qualitative.
Can data be both discrete and continuous?
Not simultaneously. A dataset is either discrete or continuous, but how it's treated can vary based on context.
How is the accuracy of continuous data ensured?
Accuracy is ensured through precise measurement tools and techniques.
How can data collection methods impact whether data is discrete or continuous?
The tool or method used can determine the type of data; for instance, a digital scale might give continuous data, while a manual count gives discrete data.
Why might someone choose to work with discrete over continuous data?
Discrete data can be simpler to analyze, especially when precise measurements aren't critical.
Can discrete data be qualitative?
Typically, discrete data is quantitative, but some qualitative data, like "yes" or "no" responses, can be treated as discrete.
How does rounding affect continuous data?
Rounding can transform continuous data into a more discrete form.
What tool is commonly used to measure continuous data?
Instruments like rulers, scales, or thermometers are used to measure continuous data.
About Author
Written by
Harlon MossHarlon is a seasoned quality moderator and accomplished content writer for Difference Wiki. An alumnus of the prestigious University of California, he earned his degree in Computer Science. Leveraging his academic background, Harlon brings a meticulous and informed perspective to his work, ensuring content accuracy and excellence.
Edited by
Janet WhiteJanet White has been an esteemed writer and blogger for Difference Wiki. Holding a Master's degree in Science and Medical Journalism from the prestigious Boston University, she has consistently demonstrated her expertise and passion for her field. When she's not immersed in her work, Janet relishes her time exercising, delving into a good book, and cherishing moments with friends and family.
