# Difference between Nominal and Ordinal Number

Nominal and ordinal numbers are both types of numbers, but they have different uses. Nominal numbers are used for labeling or identifying things, while ordinal numbers are used for ordering things. In this post, we’ll take a closer look at the differences between nominal and ordinal numbers.

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## What is a Nominal Number?

Nominal numbers are numbers that do not represent anything other than themselves. Nominal numbers can be used for labeling or identification purposes, but they have no inherent value. For example, a social security number is a nominal number because it does not represent anything other than the identification of the person to whom it was assigned. Nominal numbers are often used in research studies as a way to protect the identity of participants. In some cases, nominal numbers may be used to represent certain categories or groups (e.g., male/female, Republican/Democrat). Nominal numbers can be converted to other types of numbers (e.g., ordinal or interval), but they cannot be meaningfully compared to other numbers.

## What is an Ordinal Number?

Ordinal numbers are used to indicate position or rank in a series. They are written as first, second, third, etc. Ordinal numbers are sometimes confused with cardinal numbers, which are used to indicate quantity. However, ordinal numbers always come before the noun they modify, while cardinal numbers never do. For example, you might say “I was the third person to finish the race,” but you would never say “I finished the race with a third-place finish.” Ordinal numbers are also used to create Ordinal scale data in research. This type of data measures how subjects rank certain items on a scale from most desirable to least desirable. In essence, ordinal numbers are a way of putting things in order. By understanding what ordinal numbers are and how they’re used, you can effectively communicate your ideas and better understand the ideas of others.

## Difference between Nominal and Ordinal Number

• Nominal and Ordinal numbers are two different types of numbers used in mathematical operations. Nominal numbers are used to identify objects or persons irrespective of their quantity, while Ordinal numbers express the order or ranking of objects or persons. Nominal numbers are denoted by # symbol and are placed on the left side of the line, while Ordinal numbers are denoted by letter symbols and are placed on the right side of the line. Nominal numbers can be increased or decreased without affecting their value, but Ordinal numbers cannot be altered as they represent a sequence. For example, if there are five students in a class, then their ages can be expressed as nominal numbers like 20, 21, 19, 18, 17.
• But if their positions in a race are to be expressed, then it has to be done using Ordinal numbers like 1st, 2nd, 3rd, 4th, 5th. Nominal data can be qualitative or quantitative in nature, but Ordinal data is always qualitative. Nominal scale is an unordered scale while Ordinal scale is a ordered scale. The mode and median can be calculated for Nominal data type, but only median can be calculated for ordinal data type.
• Example of Nominally scaled variable is religion (Christianity, Islam etc), while examples of Ordinally scaled variable include Military rank (Colonel, Brigadier etc) and Census Ranking (1st class city, 2nd class city etc). Nominal and ordinal data are both categorical variables i.e., data that can be divided into groups or categories. Nominal and ordinal data can both be represented using bar graphs and pie charts. Both these data types have their own importance and uses in statistics and research.

## Conclusion

In summary, nominal numbers are used to label or identify items, while ordinal numbers are used to rank or order items. The difference between these two types of numbers is important to understand when working with data sets and trying to make accurate conclusions. When looking at survey results, for example, it’s crucial to be aware if the questions were asked using nominal or ordinal scales in order to correctly interpret the responses.