Real-world examples of descriptive statistics: mean, median, mode examples

Before definitions, let’s ground this in reality. When people ask for examples of descriptive statistics: mean, median, mode examples, they’re usually thinking about questions like:

• What is the average salary in my industry?
• What’s a typical monthly rent in my city?
• Are my test scores above average?

These are all invitations to use descriptive statistics. Here are three quick, concrete situations.

Example 1: Tech salaries with a few very high earners

Imagine a small startup with monthly salaries (in dollars):

4,000, 4,200, 4,300, 4,500, 4,700, 5,000, 18,000

Let’s pull out the three basic measures:

• Mean (arithmetic average): Add all salaries and divide by 7.

  Total = 4,000 + 4,200 + 4,300 + 4,500 + 4,700 + 5,000 + 18,000 = 44,700

  Mean salary = 44,700 ÷ 7 ≈ 6,386

• Median (middle value): Order the salaries (already ordered) and pick the middle.

  The 4th value in this ordered list is 4,500 → median = 4,500

• Mode (most frequent value): Here, every value appears once, so there is no mode.

This is one of the best examples of why mean and median can tell very different stories. If you reported the mean salary of about \(6,386, you’d give the impression that pay is higher than it really is for most employees. The median—\)4,500—better represents the typical worker because the single $18,000 salary is an outlier.

Economists and labor statisticians run into this constantly. That’s why organizations like the U.S. Bureau of Labor Statistics often report median weekly earnings rather than just means when summarizing wages.

Example 2: City rent prices with a skewed distribution

Consider monthly rents (in dollars) for studio apartments sampled from a city neighborhood:

900, 950, 1,000, 1,050, 1,100, 1,200, 2,800

• Mean rent: (900 + 950 + 1,000 + 1,050 + 1,100 + 1,200 + 2,800) ÷ 7

  Total = 10,000 → Mean = 1,428.57

• Median rent: The middle (4th) value is 1,050

• Mode: None (no repeated value)

If a real estate site advertised that “average rent is \(1,429,” that would be technically correct, but misleading for most renters. A median rent of \)1,050 better describes what a typical renter will actually pay.

This is a classic example of descriptive statistics: mean, median, mode examples showing how:

• Mean is sensitive to extreme values.
• Median is resistant to outliers and better for skewed data.

Housing researchers frequently use median home prices for this reason. For instance, the Federal Reserve and housing market reports often emphasize median prices to avoid distortion from a few luxury properties.

Example 3: Classroom test scores with a cluster

Now look at test scores (out of 100):

60, 70, 70, 70, 80, 85, 95

• Mean score: (60 + 70 + 70 + 70 + 80 + 85 + 95) ÷ 7

  Total = 530 → Mean ≈ 75.7

• Median score: The 4th value is 70 → median = 70

• Mode: 70 (appears three times)

Here, the mean is pulled up by the 95 and 85, but both median and mode sit at 70. If a teacher wants to describe the “typical” performance, 70 is a better story than 75.7, because most students are clustered around that value.

This is one of the best examples of descriptive statistics where mode actually matters: it tells you the most common outcome.

Why mean, median, and mode still matter in 2024–2025

In an era obsessed with machine learning and big data, it’s easy to underestimate how much work is still done with simple descriptive statistics. Yet in 2024 and 2025, agencies, hospitals, and companies are still leaning heavily on these measures:

• Public health: The Centers for Disease Control and Prevention (CDC) regularly reports mean and median values for things like blood pressure, BMI, and lab markers in its National Health and Nutrition Examination Survey (NHANES). You can see examples at cdc.gov/nchs.
• Education: School districts summarize standardized test performance with mean and median scores, and often use the mode to talk about the most common grade band.
• Healthcare research: Organizations like the National Institutes of Health (NIH) and Mayo Clinic routinely summarize clinical trial data with mean outcomes and median survival times.

When you see a statement like “median household income in the U.S. was about $74,580 in 2022” (based on U.S. Census data), you’re looking at a high-impact, real-world example of descriptive statistics.

More real examples of descriptive statistics: mean, median, mode examples across fields

Let’s walk through several more concrete situations. These examples of descriptive statistics: mean, median, mode examples show up in different industries and data types, not just in textbooks.

Health example: Resting heart rate data

A fitness researcher records resting heart rate (beats per minute) for a group of adults:

58, 60, 60, 62, 64, 66, 68, 110

• Mean: Total = 548 → Mean = 548 ÷ 8 = 68.5 bpm

• Median: Ordered list has 8 values; median is average of 4th and 5th values:

  (62 + 64) ÷ 2 = 63 bpm

• Mode: 60 bpm (appears twice)

The mean is pushed up by one person with a resting heart rate of 110 bpm, which might indicate a health issue or measurement error. A doctor or researcher would likely say:

• Typical resting heart rate in this group is around 63 bpm (median),
• The most common value is 60 bpm (mode),
• And they’d investigate the outlier at 110.

This is a realistic example of descriptive statistics where median and mode provide more clinically meaningful information than the mean alone. For more on heart rate norms, you can see resources from Mayo Clinic at mayoclinic.org.

Social media example: Daily likes on a post

A content creator tracks likes on seven recent posts:

120, 130, 130, 135, 150, 400, 410

• Mean likes: Total = 1,475 → Mean ≈ 210.7
• Median likes: The 4th value (ordered) is 135
• Mode: 130 (appears twice)

The mean suggests a typical post gets about 211 likes, but that’s inflated by two viral posts with 400 and 410 likes. The median (135) and mode (130) are better indicators of usual engagement.

For social media analytics, this is one of the best examples of descriptive statistics: mean, median, mode examples showing why you should not blindly trust “average” metrics in dashboards.

Sports example: Points per game

Consider a basketball player’s points scored over eight games:

8, 10, 12, 12, 14, 14, 40, 42

• Mean: Total = 152 → Mean = 19 points per game
• Median: Middle two values are 12 and 14 → Median = 13
• Mode: 12 and 14 (bimodal distribution)

If a commentator says, “She averages 19 points per game,” that sounds like consistent high scoring, but the median of 13 tells a different story: she usually scores in the low teens, with two explosive games driving up the mean.

Sports analysts who care about consistency often look at medians, modes, and full distributions—not just mean.

Retail example: Shoe sizes in inventory

A shoe store tracks the sizes of women’s shoes sold in a week:

6, 7, 7, 7, 8, 8, 9, 9, 9, 9, 10

• Mean size: (6 + 7 + 7 + 7 + 8 + 8 + 9 + 9 + 9 + 9 + 10) ÷ 11

  Total = 89 → Mean ≈ 8.1

• Median size: 6th value in ordered list is 8

• Mode: 9 (appears four times)

If the store manager wants to know which size to stock more of, the mode (size 9) is the most important descriptive statistic. Mean and median are fine summaries, but the mode directly answers the inventory question: which size sells most often?

This is a clean example of descriptive statistics: mean, median, mode examples where mode is the real star.

Education example: SAT math scores in a tutoring group

A small tutoring center records SAT Math scores for 10 students:

520, 540, 560, 580, 600, 620, 640, 680, 720, 780

• Mean: Total = 6,240 → Mean = 624
• Median: Average of 5th and 6th values → (600 + 620) ÷ 2 = 610
• Mode: None (all scores unique)

Here, the distribution is fairly balanced. Mean and median are reasonably close, and there is no mode. The center might say:

• Our students’ mean SAT Math score is 624.
• The typical (median) student scores around 610.

This is a straightforward example of descriptive statistics without extreme outliers.

How to choose between mean, median, and mode using real examples

Looking across these examples of descriptive statistics: mean, median, mode examples, some patterns emerge:

• Use the mean when your data are roughly symmetric and you care about the overall average.
  • Example: Average SAT scores in a relatively balanced group.
• Use the median when data are skewed or contain outliers.
  • Examples include: tech salaries with a few very high earners, rent prices with luxury units, heart rate data with one abnormal reading.
• Use the mode when you care about the most common category or value.
  • Examples include: shoe sizes sold, most common test score, most frequent rating (e.g., 5-star reviews).

In many real examples, analysts report more than one of these descriptive statistics to give a fuller picture.

Connecting descriptive statistics to real data sources

If you want to see live, large-scale examples of descriptive statistics: mean, median, mode examples, you don’t have to look far:

• The CDC’s National Center for Health Statistics publishes tables with mean and median values for health measures like BMI, cholesterol, and blood pressure: cdc.gov/nchs
• The National Institutes of Health (NIH) provides clinical research summaries where mean changes in lab values and median survival times are standard: nih.gov
• Universities like Harvard host open course materials that walk through descriptive statistics with real datasets: statistics - Harvard Online

These are not just textbook examples. They show how governments, hospitals, and researchers use simple descriptive statistics to guide policy, medical guidelines, and resource allocation.

FAQ: Common questions about examples of descriptive statistics

What are some everyday examples of descriptive statistics: mean, median, mode examples?

You see them constantly:

• The average rating of a product (mean of all ratings)
• The median home price in your city on a housing website
• The most common shoe size or clothing size sold in a store (mode)

These are all everyday examples of descriptive statistics that summarize large amounts of information into a few numbers.

Can you give an example of when the median is better than the mean?

A classic example of this is income. Suppose a neighborhood has many people earning around \(50,000 and one person earning \)5 million. The mean income will be pulled up dramatically by that one millionaire, but the median income will stay near $50,000. If you want to know what a typical person earns, the median is much more informative.

When is mode actually useful in real data?

Mode is especially helpful when dealing with categories or discrete choices. Examples include:

• Most common customer complaint type
• Most frequently chosen menu item at a restaurant
• Most common shoe or clothing size sold

These are practical examples of descriptive statistics where the mode directly answers the question, “What happens most often?”

Do I always need all three: mean, median, and mode?

Not always. Many reports focus on the mean and sometimes the median. Mode is not always meaningful, especially when every value is different (like unique test scores). But in skewed data or categorical data, combining them can be powerful. The best examples of descriptive statistics in professional reports often include at least mean and median, and the mode when it adds clear value.

How do descriptive statistics relate to more advanced analysis?

Descriptive statistics are the starting point for almost any analysis. Before building predictive models or running hypothesis tests, analysts summarize data with mean, median, mode, and spread (like standard deviation or interquartile range). Even in 2024–2025, with sophisticated machine learning models, these basic measures are still used to check data quality, understand distributions, and communicate findings clearly.

Mean, median, and mode are simple, but they are not simplistic. As these examples of descriptive statistics: mean, median, mode examples show, they shape how we talk about salaries, housing, health, education, and more. Used thoughtfully, they turn raw numbers into stories you can actually act on.