In statistics, the terms "nominal" and "ordinal" refer to different types of categorizable data. In understanding what each of these terms mean and what kind of data each refers to, think about the root of each word and let that be a clue as to the kind of data it describes. Nominal data involves naming or identifying data; because the word "nominal" shares a Latin root with the word "name" and has a similar sound, nominal data's function is easy to remember. Ordinal data involves placing information into an order, and "ordinal" and "order" sound alike, making the function of ordinal data also easy to remember.

#### TL;DR (Too Long; Didn't Read)

Nominal data assigns names to each data point without placing it in some sort of order. For example, the results of a test could be each classified nominally as a "pass" or "fail."

Ordinal data groups data according to some sort of ranking system: it orders the data. For example, test results could be grouped in descending order by grade: A, B, C, D, E and F.

## Nominal Data

Nominal data simply names something without assigning it to an order in relation to other numbered objects or pieces of data. An example of nominal data might be a "pass" or "fail" classification for each student's test result. Nominal data provides some information about a group or set of events, even if that information is limited to mere counts.

For example, if you want to know how many people were born in Florida each year for the past five years, find those figures and plot your results on a bar graph. The data represented on the graph have no natural ranking or ordering; the numbers simply illustrate a fact, not necessarily a preference, and are just labels that answer the question "how many?" These are nominal data.

## Ordinal Data

Ordinal data, unlike nominal data, involves some order; ordinal numbers stand in relation to each other in a ranked fashion. For example, suppose you receive a survey from your favorite restaurant that asks you to provide feedback on the service you received. You can rank the quality of service as "1" for poor, "2" for below average, "3" for average, "4" for very good and "5" for excellent. The data collected by this survey are examples of ordinal data. Here the numbers assigned have an order or rank; that is, a ranking of "4” is better than a ranking of “2.”

However, even though you have assigned a number to your opinion, this number is not a quantitative measure: Although a ranking of “4” is clearly better than a ranking of “2,” it is not necessarily twice as good. The numbers are not mathematically measured or determined but are merely assigned as labels for opinions.

## Why Knowing the Difference Is Critical

When working with statistics, you should know whether the data you are looking at are nominal or ordinal, as this information helps you decide how to use the data. A statistician understands how to determine what statistical analysis to apply to a data set based on whether it is nominal or ordinal. Ways of labeling data in statistics are called "scales"; along with nominal and ordinal scales are interval and ratio scales.

## How Nominal and Ordinal Data are Similar

Data can either be numerical or categorical, and both nominal and ordinal data are classified as categorical. Categorical data can be counted, grouped and sometimes ranked in order of importance. Numerical data can be measured. With categorical data, events or information can be placed into groups to bring some sense of order or understanding.