In statistics, qualitative data—sometimes referred to as categorical data—is data that can be arranged into categories based on physical traits, gender, colors or anything that does not have a number associated with it.

The hair colors of players on a football team, the color of cars in a parking lot, the letter grades of students in a classroom, the types of coins in a jar, and the shape of candies in a variety pack are all examples of qualitative data so long as a particular number is not assigned to any of these descriptions.

Qualitative data is contrasted with quantitative data wherein quantitative data sets have numbers associated with them that evaluate the quantity of an object or objects with shared features. Oftentimes, quantitative data is used to analyze qualitative data sets.

## Qualitative vs. Quantitative Data

It's pretty easy to understand the difference between qualitative and quantitative data: the former doesn't include numbers in its definition of traits of an object or group of objects while the latter does. Still, it can get confusing when thinking in terms of statistical attributes, which include size and dimensions, which are quantitative and not qualitative data.

In order to better understand these concepts, it's best to observe examples of particular datasets and how they can be defined. Observe which are qualitative and which are quantitative data sets in the following examples:

- The cats' have orange, brown, black, or white fur (qualitative).
- The boys have brown, black, blonde, and red hair (qualitative).
- There are four black cats and five orange cats (quantitative).
- The cake was 50 percent chocolate and 50 percent vanilla (quantitative).

Even when a particular feature or attribute of an object is qualitative, such as chocolate for the cake or black for the cats, the inclusion of a number in the data set makes it a quantitative one, though this interplay is important for the study of statistics as it provides categories for which mathematicians can then compare numerically.

## The Importance of Qualitative Data

Whereas quantitative data is important in determining the particular frequency of traits or characteristics, the sizes, and dimensions of objects, and that sort of information about a given topic, qualitative data like the color of hair or skin of employees in a company or the healthiness of a pet's coat can be important in statistical analysis, especially when paired with quantitative data about these qualitative features.

Essentially, qualitative data is important because it allows statisticians to form parameters through which to observe larger sets of data. For instance, a company that wanted to determine the diversity of its workforce would want to look at a set of qualitative data like race and ethnicity of its employees as well as the quantitative data of the frequency of employees to belong to those races and ethnicities.

Qualitative data provides the means for which observers can quantify the world around them—there are three blondes, two brunettes, and three black-haired women at the table or there are 16 freshmen and 15 sophomores attending the annual band trip.