Statisticians and analysts use data to gain valuable insight into current issues or future plans. They use different statistical methods to analyze data, in which discrete data plays a big part. To better understand discreet data, it’s important to familiarize yourself with data types through discrete data examples.
In this article, you’ll learn about different data types and the real-world applications of discrete data. It also lists the 10 best examples of discrete data, which you can draw inspiration from. If you’re interested in improving your analytical and problem-solving skills, continue reading.
What Is Discrete Data?
Discrete data consists of whole numbers with finite values. This sort of data can’t be broken down into smaller pieces or decimals. It can further be classified as a categorical or discrete variable. To give you a more relatable example, the number of friends you have is discrete data. You can only mention that data in a whole number (10 friends) but never in a decimal (10.5) or fraction (10 1/2).
When it comes to data typology, data can be categorized in many ways. Data can be structured or unstructured, quantitative or qualitative, and discrete or continuous. There are also three types of variables: discrete, categorical, and continuous variables.
Where Is Discrete Data Used?
- Statistical analysis
- Data science
- Computer science
- Mathematics
- Research
Why Is Discrete Data Important?
Discrete data is part of discrete mathematics. Because there are different kinds of data, people use them in different ways to make predictions. In most cases, they conduct simple statistical analyses to see the probability for those predictions, which makes this type of data essential when creating graphs, bars, pie charts.
Real-World Examples of Discrete Data
In the next section, we created a list of 10 real-world discrete data examples. There are many common examples of discrete data that we use in our daily life, from the weekly products we buy at the grocery store to how many doctors are in a town or state. Let’s dive into the list and understand them deeper.
- The number of children in several families
- Population analysis
- The number of children in a school class
- Marketing demographics and choice modeling
- Shoe size
- The number of pages in a book
- The Gini Index for income inequality
- IQ tests
- Gender
- Applications in statistics
10 Great Examples of Discrete Data
Discrete Data Example 1: The number of children in several families
If you want to find out the number of children in several families, you can do that with discrete data. You can use frequency tables to find out how many children each family has. All you have to do is count the frequency of the number of children in every family. You can use bar graphs, bar charts, or other visualization methods for better representation of this discrete data.
Discrete Data Example 2: Population analysis
Population analysis can use discrete and continuous data. A case where population analysis uses discrete data is if you want to find out the demographics of a particular field of work at the national level. This type of discrete data is a categorical variable. Here, most researchers use chi-square to test categorical data analysis.
Discrete Data Example 3: The number of children in a school class
The number of children in a school class is discrete data. This data has numerical values that are whole numbers because you can count all the children in one class. If there are fewer children than the maximum number, you can divide the list of present and absent children. With this, you’ll be able to create two discrete categorical variables.
Discrete Data Example 4: Marketing demographics and choice modeling
Discrete data is used in marketing in many aspects. In marketing, to identify an organization’s target consumer, you use the market segmentation technique on demographic variables. Most demographic variables like age, income, education are discrete data variables which means they undergo discrete data analysis.
In practice, once you have segmented your potential buyers, the next step is to price the product or service with discrete choice modeling. Discrete choice models are used to determine the probability that a person will choose a product among other product alternatives.
Discrete Data Example 5: Shoe size
Shoes come in different sizes and different numbers. It’s discrete data because it can only take a certain whole number value. However, do take note that while shoe size numbers can be discrete, the underlying foot length measurement is continuous data.
Discrete Data Example 6: The number of pages in a book
In most cases, books are used as study material and the pages can be considered countable items. Thus, there’s a finite number of pages in a book. A book can’t have a decimal number of pages or half a page, so the number of pages is a finite value and thus a discrete data variable.
Discrete Data Example 7: The Gini Index for income inequality
The Gini coefficient shows discrete probability distribution. Put simply, it shows inequality among values of a frequency distribution. The Gini Index or Gini ratio is most commonly used to show income inequality or wealth inequality within a social group.
Discrete Data Example 8: IQ tests
The IQ test is a discrete value because it’s a numerical variable that can be counted. IQ tests are used as a measurement of intelligence, but to be classified as continuous data, they need to provide the exact amount of intelligence. For instance, if two people have 120 IQ, we don’t know which has less or more IQ because the test doesn’t show decimals.
Discrete Data Example 9: Gender
Your gender is a qualitative discrete data variable. You can use a pie chart percentage representation of a particular gender in a certain workplace. Gender is an example of a categorical variable that has discrete data. On the other hand, a female’s or male’s weight or height has continuous data values because it uses decimal values.
Discrete Data Example 10: Applications in Statistics
Discrete data has many uses in statistics. In statistical analysis, you can choose to use discrete data like a categorical or continuous predictor. This way, you can use simple regression for one continuous predictor or use regression analysis for a dependent variable that assumes discrete values.
Pro Tips to Boost Your Discrete Data Skills
- Read and educate yourself. Read papers or books to educate yourself about discrete mathematics. Mainly, you must start differentiating between discrete and continuous data. The more basic knowledge you have, the better understanding you’ll have of discrete data as a whole.
- Enroll in STEM classes. In the science, technology, engineering, and mathematics (STEM) track, you’ll become more knowledgeable on using discrete values and discrete mathematics across these four disciplines. Online STEM courses are also available to help you learn to think like a data scientist and use discrete data in data analyses.
- Learn mathematics or statistics. By enrolling in mathematics or statistics courses, you’ll learn more about discrete data. If you want to further advance your skills, get hands-on learning of discrete mathematics by signing up for mathematics for computer science courses.
What Should Be the Next Step in My Discrete Data Learning Journey?
The next step in your journey is to constantly learn about discrete data. You’ll also need to learn about continuous data, so you can identify the key differences this data has from discrete data. Many data analysis methods use them both. So, if you want to be an expert in data science, statistics, or computer science, educating yourself on the practical applications of these two data types will help you in the long run.
Discrete Data Examples FAQ
The four types of data are nominal, ordinal, continuous, and discrete data. Qualitative data types can have data with nominal and ordinal values. On the other hand, quantitative data types can have data with continuous and discrete values.
Two statistical tests are used as statistical methods to analyze discrete data. The first and most used is the chi-square test, and the second is the Fisher’s exact test. The chi-square measures the approximate likelihood of an event occurring, while the Fisher’s exact test measures the exact probability of the observed frequencies.
Discrete data has several disadvantages. It doesn’t show the relationship between the variables. Discrete data is not easy to break down into smaller units. This kind of data isn’t as detailed as continuous data, so you can’t gain as much insight. Discrete data is also not as precise as continuous data, so it can’t be used for precise measurements.
Yes, discrete data can be a negative number. However, in most cases, discrete data does not include negatives. For example, one family can’t have minus three children, but if you take into account the scores of baseball players in a match, they can have negative scores.
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