How do you know if a variable is discrete or continuous?

A variable is discrete if it can be counted, and it is continuous if it can be measured. That means that the number of pets a household has or how many pull-ups you did in your last workout are discrete variables, while how many gallons of water in a water tower or your exact age are both continuous variables.

What s the difference between numerical and discrete data?

The difference between numerical and discrete data is that discrete data is a subset of numerical data.Numerical data is quantitative data, meaning that it can be represented by a numeric value. This includes anything that can be counted or measured. Qualitative data are other kinds of descriptors, such as what color something is.So, quantitative data is how many cars are in a parking lot, and qualitative data is what color each of them is.

Discrete Data Vs. Continuous Data: What’s The Difference?

Science Terms

Discrete and continuous are both terms that are used in data processing in order to describe different sets of data and variables. Both are quantitative or numerical data, meaning that they are represented with numbers and can be processed mathematically.

Statistics and data processing have a tendency to take on an air of mystique, but once someone explains the basic concept of what makes a data set discrete or continuous to you, it’s not a difficult concept to grasp.

Continuous data variables are typically used for measurements, such as someone’s height or weight. This is because it includes a range – someone’s height can be measured more and more precisely and additionally will change over time.

For discrete data, it’s individual numbers. This means that it’s used for values that can be counted, such as how many cars a household owns. It also doesn’t tend to vary over time – and won’t, over a set interval.

Key Takeaways:

Discrete	Continuous
Discrete data can be counted.	Continuous data can be measured.
Discrete data is visually represented by charts such as bar graphs, pie charts, and scatter plots. All of these show individual values.	Continuous data is usually represented visually by histograms or line charts, which are better for showing how the data fits together in a range.
Some common examples of discrete data are the number of children in a household, how many games a sports team won in a season, or the number of contestants in a race.	Common examples of continuous data are a person’s age, a baby’s birth weight, and the amount of time it takes a sprinter to run a quarter mile.
Discrete data will remain constant over an interval of time.	Continuous data will vary over time and have different values at different times.

What Is Discrete Data?

Discrete data variables are always fixed. This type of data is typically used in situations such as counting how many of something there are, such as a census, or how many cars a family owns. It isn’t possible to have 2.4 cars, nor is it possible to drill down to be more precise than the number of cars they have.

This is not to say that a discrete variable can’t have a decimal point in it. A measurement such as someone’s shoe size is a discrete variable, as there are only so many values that it can hold. It can be 10.5, for instance, but as shoe sizes are only in wholes and halves, there’s a limit to what it can be. And this makes discrete data inherently less precise than continuous data.

While this data can be averaged, which is where the whole idea of households having 2.3 kids comes from, it isn’t actually possible for a household to have a partial child. This is what makes the data discrete – there are a finite number of values that can be assigned.

Discrete data is most often plotted with the typical types of graphs you see – bar graphs, scatter plots, and pie charts.

What Is Continuous Data?

Continuous data is a value that can be found within a range. This largely has to do with rounding, as measurements can be done with ever-increasing accuracy, depending on the measurement tools and how long you want to spend filling in decimal spaces.

Continuous data are going to be values such as temperature, length, age, and weight. All of these measurements can be more precise to an infinite degree. For example, if you want to measure a dog’s weight, you can do it in stones, pounds, or ounces. And then you can go down into partial ounces, approaching true accuracy as a limit.

This makes continuous data much more accurate than discrete data. While continuous data can be pushed out to even more precision, discrete data is limited to a single value.

Continuous data is most often visually represented with a histogram or a line chart, both of which show the continuous nature of the data. It isn’t just a single point but a range of values that fit together.

Discrete Data vs. Continuous Data FAQ

How do you know if a variable is discrete or continuous?

A variable is discrete if it can be counted, and it is continuous if it can be measured. That means that the number of pets a household has or how many pull-ups you did in your last workout are discrete variables, while how many gallons of water in a water tower or your exact age are both continuous variables.
What’s the difference between numerical and discrete data?

The difference between numerical and discrete data is that discrete data is a subset of numerical data.

Numerical data is quantitative data, meaning that it can be represented by a numeric value. This includes anything that can be counted or measured. Qualitative data are other kinds of descriptors, such as what color something is.

So, quantitative data is how many cars are in a parking lot, and qualitative data is what color each of them is.

Science Terms

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Author

Di Doherty

Di has been a writer for more than half her life. Most of her writing so far has been fiction, and she’s gotten short stories published in online magazines Kzine and Silver Blade, as well as a flash fiction piece in the Bookends review. Di graduated from Mary Baldwin College (now University) with a degree in Psychology and Sociology.

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