Nominal Data

The idea of nominal data is to label variables without providing any quantitative value. Because the numbers used to label the variables are taken from a single set, the interpretation of these data can be difficult.Multiple sets of data can yield multiple interpretations of the same phenomena.

For example, if a test is done on a group of people who are all overweight, then the results may show that there is an association between obesity and heart disease among those who were overweight. This can provide some useful information for health-care providers in choosing which patients to treat with diet and exercise programs as well as determining which patients will benefit from surgical treatments.

There are many different kinds of nominal data that can be used in statistics: categorical (also known as ordinal or interval), numerical (also known as nominal), and geometric (also known as ordinal).

Categorical data includes two-dimensional numbers, such as height or weight;

numerical data includes numbers along a scale;

geometric data includes number lines with constant widths; and ordinal data provides a numeric scale that goes from 0 to 1 with each step decreasing in value.

Nominal data types

Nominal Data is used to make comparisons between two sets of numbers in the same scale. These numbers can also be used to compare two sets of numbers from different scales. The difference between nominal data and ordinal data is that nominal data are not as precise or specific as ordinal data, but still serve a purpose for the user.

A common example of nominal data is the label on a price tag: “This item costs $9.99”.When we want to compare two items (or behavior) on price tags, we use nominal data labels: “This item costs $9.99” versus “This item costs $10”, “This item costs $15”, etc.In some cases, nominal data are known as categorical data or categorical scales (see above). Nominal scales are not mutually exclusive!

There can be more than one level in a variable, and each level has its own specific numerical value. Examples include:- “Car” => purchase price of car;- “First Name” => first name used for calling;- “Last Name” => last name used for calling; …- “8th Grade” => first grade from 8th grade; …The reason there is more than one level varies from person to person, but what’s important is that there are two or more levels of the variable.

Advantages of nominal data

The nominal data is a type of data. It is not a code, but it is a representation of some of the values that you want to give its value. You can label variables not according to the values they have. You label them as if they were numerical values. The reason for that is that it is easier for people to understand the numbers than the words, unless one knows how to make words mean something by using the right letters.In fact, we call it “numerical data,” because this is what we use when we need to label numbers in order to make comparisons between them or between them and other numbers.

Data collected on real life objects are classified as nominal values, while those obtained on statistical objects are classified as numerical ones. For example, if you want to know how well an object will perform in a certain condition, use nominal data; if you want to know its average speed, then use numerical ones; when you want to know whether an object is heavier or lighter than its predecessor in terms of weight or mass (in other words, does it change weight or mass), then use quantitative ones—and so on through out this article!

The essence of a nominal value lies in the fact that it represents one single trait or property of an object in real life and not two separate ones (as with numerical data). It’s just one single number; more precisely one pair of numbers: the first representing one property (for example mass) and the second another (for example weight); there’s no question about which number should be considered ‘first’. In other words, we can say “the faster this car goes” without any ambiguity whatsoever; there’s only one number here which has to be considered. In contrast with this there are two numbers used for producing numeric values for statistical objects: neither here nor there at all!

Disadvantages of nominal data

Nominal data is a very popular form of data collection and reporting. Nominal data are sometimes created in order to simplify what could otherwise be a complex and cumbersome narrative. They are often used for the purpose of making quantitative comparisons.In the context of statistics, nominal data can be defined as “data which has no numerical value, such as the weight of livestock or the duration of a pregnancy.”In statistics, nominal data can be used to represent actual observations from human subjects.

However, it’s important to note that this may not always be true when it comes to survey responses or other non-numerical forms of data.The disadvantages of using nominal data in statistics include:

1) The lack of a measure that indicates an actual value, making it difficult to compare results across different studies (i.e., by gender).

2) The inability of researchers to accurately determine whether observed behavior or attitudes are truly representative (i.e., by gender).

3) The difficulty in comparing results between studies due to varying sample sizes and methodologies (i.e., by gender).

4) Difficulties in determining causality due to the inherently subjective nature of measured responses (i.e., by gender).

5) Confusion about differences between men’s and women’s behavior with regard to these issues because men may prefer being told “I don’t know” than women may prefer “I do.”

This can cause researchers to test certain behaviors as if they were part of an overall pattern and then mistake these results for meaning something else entirely (i.e., by gender).


It’s been said that we are all born as numbers. That is true to a degree, but most people don’t realize how little of what they think of as their “self” is made up of their thoughts.

In other words, you don’t actually have unique mental characteristics that make you different from the other people around you — you just have one or two personal attributes.With this in mind, the goal of nominal data is to provide a label for variables without providing any quantitative value.The problem with doing so is that it can result in confusion and miscommunication when used incorrectly.

While it’s important to avoid using nominal data in situations where its potential misinterpretation may be catastrophic, it’s also important to understand what its purpose is and how to use it appropriately.