Quantitative data definition
Quantitative data is anything that can be measured or counted. This is also called numeric data because it deals with numbers.
There’s a wide range of quantitative data examples in statistics such as monthly revenue, distance of a race and time of the winner, calories in a meal, temperature, salary, etc.
What are the different types of quantitative data?
There are two types of quantitative data: continuous data and discrete data.
- Continuous data: this is information that can be measured. It refers to one point within a range (or continuum). Technically, continuous data can be infinitely more precise. For example, if you use a scale at home, your dog may weigh 35 pounds. But the veterinarian’s scale might show more precisely that the dog weighs 35 pounds and 7.63 ounces. Other examples of continuous data include the speed of a car, the weight of a toddler, the time a train departs, and the rate of revenue growth.
- Discrete data: this is information that can be counted. Generally, discrete data contains integers (i.e. finite values) and cannot be more precise. For example, the number of goldfish in an aquarium is discrete since they can be physically counted and it’s impossible to have 3.7 goldfish. Other examples of discrete data include number of customers, number of languages a person speaks, and number of apps on your phone.
What’s the difference between quantitative and qualitative data?
The terms quantitative and qualitative data are often mentioned together, so it’s important to understand the distinction between the two.
Quantitative data is information that measures or counts. This involves numbers.
Qualitative data is information that describes and categorizes. This involves qualities such as the color of the sky, the smell of perfume, music genres, or coffee bean flavors. There’s a more in-depth definition of qualitative data with more detail and examples.