Data is everywhere and we are bombarded by it every single day. Some examples of data include, weather data, streaming services, nutrition labels, social media, and much more. Categorical data helps us sort all this data that’s being thrown at us, specifically it helps us organize our observations into distinct categories. Unlike numerical data which can only be measured with numbers, categorical data can be sorted into labels or categories. In this post, I intend to explore the real-world application of categorical data in our lives and highlight its importance.
Categorical data can be identified broadly in 2 types: nominal and ordinal. Nominal data is labeled data without any order. For example, colors (red, blue, yellow) or types of vehicles (SUV, sedan, truck) can be considered nominal data. In contrast, there’s ordinal data, which is data in categories with an order, like school grades (A, B, C) or satisfaction ratings (very dissatisfied to very satisfied). While both types are used to group information, ordinal data adds the extra dimension of relative ranking among categories.
Data can play a surprising role in everyday decisions, like choosing a reusable tote over a plastic bag. There are many real-world examples of categorical data behind the scenes. For instance, streaming platforms like Netflix use nominal data—such as your favorite genres—to show you the next show you might like. Online retailers, such as eBay, use a mix of categorical and numerical data when you search for a “medium, red hoodie under $40,” which helps refine results to your preferences. Even something as simple as asking lemonade stand customers what they prefer: paper, plastic, or reusable cups can help guide sustainable decisions. In schools, student surveys that ask whether students prefer group projects or solo work can shape how lessons are delivered. In city planning, when communities give feedback on local parks—like requesting splash pads or picnic areas—that categorical data directly influences how public spaces are designed. Whether it’s entertainment, shopping, education, or sustainability, these little data points help shape bigger, smarter decisions every day.
Once categorical data is collected, analysis typically begins with calculating frequency counts and percentages—like finding out that 60% of customers prefer reusable cups. Analysts often use cross-tabulations to uncover relationships between different categories, such as comparing preferences across age groups or regions. To visualize how responses are distributed, bar charts and pie charts are commonly used, offering a quick snapshot of trends. For deeper insights, statistical tools like chi-square tests help determine whether observed differences between categories are meaningful or just due to chance.
Every single day, categorical data can help give us insights into people’s personalities. Whether you’re designing a product, writing a report, or solving a social issue, understanding how to gather and analyze this kind of data is a valuable tool. So the next time you see categorical data, smile when you realize it’s not all numbers and complicated graphs, it’s what helps us decide everything in our chaotic everyday lives.
Sources:
1. GeeksforGeeks. “Categorical Data.” GeeksforGeeks, 17 Mar. 2023, https://www.geeksforgeeks.org/categorical-data/
2. Centilio. “What Is Categorical Data?” Centilio, https://centilio.com/resources/what-is-categorical-data/. Accessed 11 July 2025.
3. Kanagavell, Maheshh. “Statistics for Data Science: Key Concepts and Real-World Applications.” Medium, 4 Apr. 2023, https://medium.com/@maheshhkanagavell/statistics-for-data-science-key-concepts-and-real-world-applications-b9001d871008.
4. Nerd for Tech. “Understanding the Chi-Square Test: Applications and Real-World Examples.” Medium, 16 Mar. 2023, https://medium.com/nerd-for-tech/understanding-the-chi-square-test-applications-and-real-world-examples-956e005a7104.
