**It’s called categorical, or nominal data.****Is this****statement TRUE or FALSE?**

The aim of this problem is to familiarize us with the concepts of **variables** that can be measured and can assume **distinct** values and can have different **qualitative** and **quantitative** characteristics.

Variables are categorized into two categories: **categorical** and **numeric**. Each category is ranked into two subcategories: **nominal** or **ordinal** holds categorical variables, and **discrete** or **continuous** holds numeric variables.

## Expert Answer

To solve this problem, we will look into a few perspectives of **classification** and **characterization**. The first perspective is that we can define the **nominal** or **ordinal** data categories as the domains of the $4$ data measurement rankings in **statistics** and research, whereas the other two belong to **interval** and **ratio** data. These $4$ data measure rankings are subcategories of categorical and numeric data as we have discussed above.

Furthermore, these **nominal** and **ordinal** data classes are organized under the **categorical** class, while **interval** and **ratio** data are classified under the **numeric** data class. This categorization is found to be on the quantitativeness of a data model.

**Categorical** data is not quantitative data, which means it does not possess a **numerical** value. Thus, both nominal and ordinal data can be declared as** non-quantitative**, which may represent a string of text, date, or any other alphabetical sentence.

Now coming onto the $2^{nd}$ perspective that is the nominal data can be described as data that is employed for **labeling variables**, without any numerical value. Sometimes we call this type of data- “**named”** data – which is coined from the phrase **nominal**.

To further understand nominal data, we can come up with a simple example of **Race,** which is a nominal variable having a **diverse set** of categories, but there can not be a hard and fast rule to categorize it from **highest to lowest **or from **lowest to highest.**

Likewise, **ordinal** **data** is a kind of categorical data with an **order**. In ordinal data, variables are listed in an ordered manner. The ordinal variables are mostly numbered so as to denote the order of the list. Nevertheless, the **numbers** are not mathematically calculated or determined but are simply assigned as labels for opinions.

Thus, we can conclude that the group of **non-parametric variables** belongs to the nominal data while the group of **non-parametric ordered variables** belongs to the ordinal data.

## Numerical Result

**Qualitative data** which cannot be classified is called categorical, nominal data. Thus, the provided statement is **TRUE**.

## Example

**Primary, Master’s, Ph.D., high school, **and **bachelor’s** are all nominal data when viewed separately. But when ranked on a scale and organized in a given hierarchy (**Primary, high school, bachelor’s, Master’s, and Ph.D.**), they are viewed as ordinal data.

The primary **qualitative** disparity between ordinal and nominal data is that there lies an order set in **ordinal** **data**. Since these data types are based on a categorical nature, their **mean** and **standard deviations** cannot be calculated on a particular scale.

Thus, **qualitative** or **categorical** data can’t be estimated or measured in the form of numbers.