# 6 Types of Data in Statistics & Research: Key in Data Science

## Understanding the different types of data (in statistics, marketing research, or data science) allows you to pick the data type that most closely matches your needs and goals.

If you're a businessman or marketing professional, data scientist or any other professional working with a variety of types of data, you must know the essential kinds of data.

Why? Because the different classifications of data enable you to use the correct measurements and , consequently, to make choices.

Here's the page

- The most popular types of data
**(with an example)**in statistics, research or data science. Simply explained. - Infographics in PDF

*(image source: https://www.intellspot.com/data-types/ )*

**Qualitative and Quantitative Data**

**1. Quantitative data**

Quantitative data is the most straightforward to explain. It provides answers to the most important questions, such as "how many" "how many" as well as "how frequently".

Quantitative data may be represented as numbers or quantified. In simple terms, it can be measured with numerical variables.

Quantitative data is easily adapted to statistical manipulation, and can be displayed using a array of kinds of graphs and charts, such as bar graph, line graph scatter plot, others.

**Quantitative data examples:**

- Test scores and exam scores e.g. 85 90, 67, others.
- The weight of an individual or an object.
- The shoe you wear is the size of yours.
- The temperature of the room.

There are two general kinds of quantitative data that are discrete or continuous information. We'll explain these later in this article.

**2. Qualitative data**

Qualitative data cannot be represented in numbers and cannot be quantified. Qualitative data are composed of pictures, words and symbols, not numbers.

Qualitative data can also be referred to as categorical or categorical data since the data can be classified by categories, not just by numbers.

Qualitative data may answer questions like "how this happened" or "why this happened".

**Examples of data that is qualitative**

- Colors e.g. the hue of the ocean
- Your top holiday spot like Hawaii, New Zealand and others.
- Names such as John, Patricia, John, Patricia ,.....
- Ethnicity like American Indian, Asian, etc.

You can find more information in our blog post qualitative vs . quantitative data.

There are two types of qualitative data that are nominal as well as ordinal. We'll explain them in some time.

**Nominal vs . Ordinal Data**

**3. Nominal information**

Nominal data is utilized only for labeling variables, and without any quantifiable value. The term "nominal" comes in the Latin phrase "nomen" which is a synonym for 'name''.

The nominal data simply name something without using it to create an the context of. In reality the nominal data could be described as "labels."

**Examples of the Nominal Data**

- Gender (Women, Men)
- Hair color (Blonde, Brown, Brunette, Red, etc.)
- Marital status (Married, Single, Widowed)
- The Ethnicity (Hispanic, Asian)

As you can see in the examples, there is no fundamental order in the data.

The color of the eyes is noun variable that has a couple of types (Blue, Green, Brown) and there's no way to rank these categories from the highest to the lowest.

**4. Ordinal data**

Ordinal data indicates where numbers are in order. This is the key difference between nominal and ordinary types of data.

Ordinal data refers to data that is put into a certain sort of order based on their location on the scale. Ordinal data can suggest superiority.

But, **you cannot do the arithmetic using ordinal numbers** since they show the sequence.

Ordinal variables are thought of by some to fall "in between" quantitative and qualitative variables.

Also, ordinal data are qualitative data in which the values are classified.

In contrast to non-nominal data other kind of data is qualitative, for which the data cannot be arranged in an order.

Additionally, we can give numbers in ordinal data in order to indicate their relative positions. However, we are not able to perform math using those numbers. For instance: "first, second, third...etc."

**Common Data Examples:**

- The first person, second and third in a race.
- Letter grades A B,, the like.
- If a business asks customers to rate their customer's experience on the scale 1-10.
- Economic situation high, medium, and low. high.

**Discrete vs Continuous Data**

As we've mentioned before, continuous and discrete data are the two main kinds in quantitative information.

In the fields of statistics, marketing research as well as data science, a lot of decisions are based on whether the primary data is continuous or discrete.

**5. Discrete data**

Discrete data refers to a number that is based on only integers. The discrete value are not subdivided into pieces.

For instance the number of students in a class is a discrete information. It is possible to count entire individuals. It is not possible to count 1.5 children.

In another way the term "discrete data" means that it can have just a few values. The variables that comprise the data cannot be separated into smaller parts.

It can only be used for a small amount of values that can be used e.g. dates of each month.

**Examples of discrete data**

- There are many students in the class.
- The number of employees working within a business.
- The amount of home runs that occur in an MLB game.
- The number of questions on the test you correctly answered

**6. Continuous data**

Continuous data can be effectively divided into smaller levels. It can be measured using an x-axis or a continuum, and could have any numerical value.

You can, for instance, determine your height on extremely precise scales- millimeters, centimeters, meters and so on.

You can capture continuous data in a variety of measurement points - temperature, width as well as time. It is here that the major distinction between discrete and continuous data is.

The continuous variables can be used to represent any value that falls between two numbers. For instance Between 50-72 inches there exist millions of heights that could be possible: 52.04762 inches 69.948376 inches, etc.

One great way of the determination of whether a data is discrete or continuous is that if the area of measurement can be cut by half and still be logical the data is considered to be continuous.

**Examples of continuously storing data**

- The time it takes to finish a project.
- The size of children.
- The area of a two-bedroom home.
- The speed of automobiles.

# 6 types of data that are used for Statistics & Research: Key to Data Science

Understanding the various types of data (in marketing research, statistics as well as data science) lets you choose the type of data that most closely aligns with your needs and objectives.

If you're a businessman or marketing professional, data scientist or any other professional who deals with a variety of types of data, you must be aware of the most important listing of types of data.

Why? Because the various classifications of data permit you to make the most accurate use of measurements and , consequently, to make choices.

Here's the page

- The most popular types of data
**(with an example)**in statistics, research as well as data science. Simply explained. - Infographics in PDF

**Qualitative Vs Quantitative Data**

**1. Quantitative data**

Quantitative data is the most straightforward to explain. It provides answers to the most important questions, such as "how many, "how many" or "how oft".

Quantitative data may be expressed in terms of numbers or quantified. Simply put, it could be measured with numerical variables.

Quantitative data can be easily subjected to statistical manipulation, and can be represented using a array of kinds of graphs and charts, such as bar graph, line graph scatter plot, other.

**Quantitative data examples:**

- Scores from tests and exams e.g. 85 90, 67, others.
- The weight of an individual or an object.
- The size of your shoe.
- The temperature of a space.

There are two types of quantitative data such as discrete data as well as continuous data. We'll explain these later in this article.

**2. Qualitative data**

Qualitative data cannot be represented in numbers and isn't measurable. Qualitative data are composed of pictures, words and symbols, not numbers.

Qualitative data is also known as categorical information because it can be sorted according to category, not by numbers.

Qualitative data can help answer questions like "how this happened" or "why this is happening".

**Qualitative data examples**

- Colors e.g. the colors of the ocean
- Your top holiday spot like Hawaii, New Zealand and others.
- Names such as John, Patricia, John, Patricia ,.....
- Ethnicity, such as American Indian, Asian, etc.

More information can be found in our blog post qualitative vs . quantitative data.

There are two types of qualitative data that are nominal as well as ordinal. We'll go over them in some time.

Download the infographic below in PDF

**Ordinal vs. Nominal**

**3. Nominal information**

Nominal data is utilized only for labeling variables, and without any kind of quantifiable value. The term "nominal" comes in the Latin term "nomen" which means "name"'.

The nominal data is just a name for an object without making it a part of an the order. In reality the nominal data can simply be referred to as "labels."

**Examples of the Nominal Data**

- Gender (Women, Men)
- Hair color (Blonde, Brown, Brunette, Red, etc.)
- Marital status (Married, Single, Widowed)
- The Ethnicity (Hispanic, Asian)

As you can see in the examples, there is no inherent order in the data.

Color of eyes can be described as a subjective variable that has a few types (Blue, Green, Brown) and there isn't a way to rank these categories from top to the lowest.

**4. Ordinal data**

Ordinal data indicates where the numbers are arranged. This is the main difference between nominal and ordinary types of data.

Ordinal data refers to data that is put into a certain kind of order through their location on an scale. Ordinal data can be a sign of superiority.

In reality, **you cannot do math with ordinal numbers** since they display the sequence.

Ordinal variables are regarded by some to fall "in the middle of" both quantitative and qualitative variables.

That is, ordinal data are qualitative data that is placed in a particular order.

When compared to non-nominal data other kind of data is qualitative, for that the values can't be put in an order.

It is also possible to give numbers in ordinal data in order to illustrate their relative positions. However, we can't perform math using those numbers. For instance: "first, second, third...etc."

**Common Data Examples:**

- The first person, the second and the third in a contest.
- Letter grades A B, C the like.
- If a business asks customers to rate their satisfaction of the sales experience using an scale of 1-10.
- Economic situation Low, medium and high.

A lot more information about the subject as well as a test, you can read about it in our blog: nominal vs ordinal data.

Download the infographic below in PDF

**Discrete vs Continuous Data**

As we have mentioned previously, Continuous and discrete data comprise two primary kinds of data that can be quantified.

In marketing research, statistics and data science, a lot of decisions depend on whether the fundamental data is continuous or discrete.

**5. Discrete data**

Discrete data refers to a number that is based on only integers. The discrete value are not subdivided into pieces.

For instance the number of kids in a class is a discrete information. You can count individuals as whole. It is not possible to count 1.5 children.

In the other way the term "discrete data" means that it can have just a few values. Data variables can't be separated into smaller parts.

It is a finite range of possible values e.g. the days during the month.

**Examples of data that are discrete:**

- A number of pupils in the class.
- The number of employees working within a business.
- The number of home runs during the course of a baseball game.
- The number of questions on the test you correctly answered

**6. Continuous data**

Continuous data can be effectively divided into smaller levels. It is measured on an x-axis or a continuum, and could have any numerical value.

For instance, you could determine your height on extremely precise scales- millimeters, centimeters and meters and more.

Continuous data can be recorded in a variety of measurements like width, temperature time, width, etc. It is here that the major difference between discrete and continuous data comes into play.

Continuous variables can be used to represent any number between two numbers. For instance from 50 to 72 inches millions of heights that could be possible: 52.04762 inches, 69.948376 inches, etc.

An excellent rule of thumb for the determination of whether a data is discrete or continuous is that if its point of measurement is reduced by half and still be logical the data is continuous.

**Examples of continuously storing data**

- The time it takes to finish a project.
- The size of children.
- The area of a house with two bedrooms.
- The speed of automobiles.

More information on this topic you can read in our comprehensive article Continuous vs. discrete data which includes the chart of comparison.

Download the infographic below in PDF

**Conclusion**

Every type of data play a vital place in research, statistics as well as data science.

Data types can be used to aid businesses and organizations across all industries develop a successful decisions based on data..

Being in the data management area and having a broad variety of skills in data science requires a thorough knowledge of different kinds of data and the right time you should use these types of data.