Real-world examples of frequency distribution: data you actually use

Quick tour of real examples of frequency distribution

Let’s skip the theory and start with data. Below are several real examples of frequency distribution drawn from contexts you actually care about: education, health, money, and online behavior. Along the way, I’ll point out why each example of frequency distribution is useful and what patterns you can pull out in seconds.

Example of frequency distribution: test scores in a high school class

Imagine a U.S. high school algebra class with 30 students. The teacher wants to summarize the distribution of scores on a 100-point exam. The raw scores look like a mess:

52, 58, 60, 61, 62, 65, 68, 69, 70, 70, 71, 72, 73, 74, 75, 76, 78, 80, 82, 84, 85, 88, 90, 92, 93, 95, 96, 98, 100, 100

Instead of staring at 30 numbers, the teacher groups them into score ranges (class intervals). Here’s a simple grouped frequency distribution:

This is one of the best examples of how frequency distributions simplify data:

• You instantly see the most common score range (70–79).
• You can tell the class is skewed toward higher scores; there are more students in the 70+ ranges than below 70.
• It sets you up to draw a histogram or compute descriptive statistics like mean and median.

Schools and testing organizations use this sort of summary constantly. For instance, the College Board publishes score distributions for the SAT and AP exams so educators can compare local results to national patterns.

Health data examples of examples of frequency distribution

Health statistics are packed with examples of frequency distribution because public health agencies need to see patterns across age, region, and time.

COVID-19 cases by age group

Suppose a local health department summarizes weekly COVID-19 cases by age group. A simplified example of a frequency distribution for one week might look like this:

Here, each age group is a class, and the frequency is the count of cases.

Patterns you can read immediately:

• The 30–49 group has the highest case count.
• Combined, working-age adults (18–64) account for most cases.
• The 0–17 and 65+ groups have fewer cases in this snapshot.

Real-world data is more detailed, but this structure mirrors how the Centers for Disease Control and Prevention (CDC) presents age-stratified data in dashboards and reports (cdc.gov). The examples of examples of frequency distribution in those dashboards help policymakers decide where to focus vaccination campaigns and testing.

BMI categories in a clinic

At a primary care clinic, a physician might summarize patients’ body mass index (BMI) categories. A simple frequency distribution could be:

This example of frequency distribution tells the clinic how common each BMI category is in its patient population. That’s immediately more useful than a random list of individual BMI values.

Organizations like the National Institutes of Health (NIH) and Mayo Clinic publish BMI guidelines and related statistics that can be summarized with similar frequency tables (nih.gov, mayoclinic.org).

Income brackets: examples include pay distribution in a company

Money data is rarely evenly spread. Frequency distributions make that obvious.

Imagine a mid-sized U.S. tech company with 120 employees. HR wants to analyze annual salaries (rounded to the nearest thousand dollars):

From this real example of frequency distribution, HR can see:

• Most employees (40) fall in the \(60k–\)79k range.
• A smaller group sits in higher salary bands, hinting at a right-skewed distribution (a long tail of high earners).
• The company can compare this pattern with industry salary surveys or national wage data from the U.S. Bureau of Labor Statistics.

This is one of the best examples of how frequency distributions support pay equity audits, budgeting, and hiring strategy.

Social media analytics: examples of frequency distribution in engagement data

If you work with online content, you’re surrounded by examples of frequency distribution without realizing it.

Video views per day

Consider a creator tracking daily views on a short-form video platform over 60 days. Instead of staring at 60 numbers, they group views into ranges:

This example of frequency distribution shows:

• Most days cluster between 1,000 and 9,999 views.
• There are a handful of “viral” days above 20,000 views.
• The creator can focus on what content was posted on the high-frequency days in the 10,000–19,999 range.

Click-through rates on email campaigns

A marketing team might track click-through rate (CTR) across 80 email campaigns:

Examples of examples of frequency distribution like this help the team see where “typical” performance lands and which campaigns were outliers worth studying.

Education and assessment: examples include grade distributions and attendance

Let’s go back to education, but this time with a broader lens.

Grade distribution in a college course

A college statistics course with 200 students reports final letter grades:

This is a categorical frequency distribution. You’re not grouping numeric ranges; you’re counting categories.

Patterns:

• Most students earned B or C.
• Fewer than 10 students failed.
• The instructor can compare this pattern with previous semesters to see if changes in teaching style shifted the distribution.

Class attendance across the week

A school might track how many students are absent each day across a semester. Suppose they summarize the number of days with certain absence counts:

This example of frequency distribution helps the school identify how often high-absence days occur and whether they cluster around specific times (e.g., flu season or just before holidays). For context on attendance and health-related absences, school districts often align with public health guidance from sources like the CDC.

E-commerce and customer behavior: examples of examples of frequency distribution

Online retailers live on data. Many of their dashboards are, under the hood, frequency distributions.

Number of items per order

A small online store analyzes 1,000 recent orders:

What stands out in this real example of frequency distribution:

• Single-item orders dominate, but multi-item orders still represent a sizable share of revenue.
• The marketing team might push “buy 2, get 10% off” promotions to shift customers from the 1-item category into the 2–3 item range.

Order value distribution in dollars

They also summarize order values:

Again, this is an example of frequency distribution that reveals:

• The most common order value range is \(25–\)49.99.
• There’s a long tail of high-value orders, which may warrant special retention strategies.

How to read and compare different examples of frequency distribution

Now that we’ve walked through several examples of examples of frequency distribution, it’s worth pulling out a few patterns in how they’re built and interpreted.

Grouped vs. ungrouped

• Ungrouped distributions list each distinct value and its frequency. These work when the data has a small number of distinct values (like letter grades A–F).
• Grouped distributions use intervals, like salary ranges or age bands. These are better when you have many possible values (exam scores from 0 to 100, income from \(0 to \)300,000).

Most real examples, especially with large datasets, use grouping because it keeps tables readable.

Categorical vs. numerical

• Categorical: BMI categories, letter grades, product categories. The classes are labels.
• Numerical: age, income, test scores, order value. The classes are numeric intervals.

Being clear about which type you’re working with matters when you move on to charts (bar charts vs. histograms) and later statistical analysis.

Shape of the distribution

When you scan examples of frequency distribution, you’re really trying to see the shape:

• Symmetric: frequencies rise to a middle peak and then fall (often seen in test scores when the exam is well-designed).
• Right-skewed: a lot of low-to-middle values, with a tail of high values (income, order value, daily views).
• Left-skewed: a lot of high values, with a tail of low values (for example, an easy quiz where most students score high).

Recognizing the shape helps you decide which summary statistics and models make sense next.

Why 2024–2025 data trends still rely on classic frequency distributions

Even with modern machine learning and streaming analytics, the basic frequency distribution has not gone out of style. If anything, it’s more relevant:

• Public health: Agencies like the CDC still publish case counts, hospitalizations, and mortality by age, region, and time period using standard frequency tables. These are the starting point for more advanced modeling.
• Education analytics: Universities and school districts summarize test scores, completion rates, and course evaluations with distributions before they try predictive models. Many institutional research offices at major universities (for example, those linked from harvard.edu) publish tables that are, effectively, frequency distributions.
• Business dashboards: Whether it’s income brackets in HR systems, churn rates in SaaS products, or purchase frequency in retail, the examples of examples of frequency distribution you see in dashboards shape strategic decisions.

The takeaway: if you can read and construct a clear example of frequency distribution, you can understand a surprising amount of what’s happening in modern data-driven organizations.

FAQ: short answers with real examples

What is a simple example of frequency distribution?

A simple example of frequency distribution is a table of test scores grouped into ranges, such as 0–59, 60–69, 70–79, 80–89, and 90–100, with a count of how many students fall into each range. This immediately shows which score band is most common.

What are some real examples of frequency distribution in everyday life?

Real examples include: the number of customers visiting a store each hour, daily step counts on a fitness tracker summarized into ranges, age groups in a population report, or salary brackets in a company. Anytime you see data grouped into ranges or categories with counts, you’re looking at a frequency distribution.

How are frequency distributions used in health and medicine?

Health organizations use them to summarize cases, risk factors, and outcomes. For example, the CDC might report influenza cases by age group and state, while a hospital summarizes patient stays by length of stay ranges. Sites like the CDC and NIH often publish tables that are textbook examples of frequency distribution.

Why use a frequency distribution instead of just listing all the data?

Listing raw data hides patterns. A frequency distribution organizes data so you can see where values cluster, where they’re rare, and how the distribution is shaped (symmetric, right-skewed, left-skewed). That’s the first step before building histograms, calculating summary statistics, or running more advanced analysis.

Are histograms and frequency distributions the same thing?

Not exactly. A histogram is a visual representation of a frequency distribution, typically for numerical data grouped into intervals. The underlying frequency table comes first; the histogram is just a way to display it.

The short version: once you start noticing them, you’ll see examples of frequency distribution everywhere—from grade reports to public health dashboards to your own app analytics. The examples include almost any situation where you’re counting how often something happens, then organizing those counts into a table you can actually read.