The best examples of summary statistics: practical examples that actually matter

Statistics only gets interesting once you see it in the wild. So instead of starting with definitions, let’s walk through real examples of summary statistics in action, then unpack the tools behind them.

We’ll move through:

• Salaries and income
• Medical research
• Sports analytics
• Housing markets
• Education and test scores
• Finance and investing
• Public health and epidemiology
• Social media and digital products

Along the way, you’ll see how the best examples of summary statistics usually combine several measures: center (mean/median), spread (range, standard deviation, IQR), and shape (skew, outliers).

Salary and income: examples of summary statistics that reveal inequality

Imagine a tech company with 100 employees. HR publishes this one number:

Average salary: $150,000

Sounds great. But that single average hides a lot. Here’s a more honest set of examples of summary statistics: practical examples for the same company:

• Mean (average) salary: $150,000
• Median salary: $105,000
• Minimum / maximum: \(60,000 – \)900,000
• Standard deviation: $120,000
• 10th percentile: $70,000
• 90th percentile: $300,000

Now the story changes. The median (\(105k) is far below the mean (\)150k), which tells you salaries are right-skewed: a few very high earners (founders, executives) pull the average up. The wide standard deviation and big gap between the 10th and 90th percentiles show serious inequality.

This kind of example of summary statistics is exactly how economists talk about income distribution in the real world. Agencies like the U.S. Census Bureau publish income medians and percentiles for states and cities, because medians are more stable than means when a few ultra-high incomes are in the mix.

Takeaway: When data are skewed (like income), the median and percentiles are often more informative than the mean.

Medical trials: examples of summary statistics that decide if a treatment works

Clinical trials live and die on summary statistics. Suppose researchers test a new blood pressure drug against a placebo in adults with hypertension.

After 12 weeks, they summarize systolic blood pressure change (in mmHg) in each group:

• Treatment group (n = 500)

  • Mean change: −12 mmHg
  • Standard deviation: 8 mmHg
  • Median change: −11 mmHg
  • 25th / 75th percentiles: −16 / −7 mmHg

• Placebo group (n = 500)

  • Mean change: −4 mmHg
  • Standard deviation: 7 mmHg
  • Median change: −3 mmHg
  • 25th / 75th percentiles: −8 / 0 mmHg

These examples of summary statistics: practical examples let you see at a glance:

• The drug group’s mean drop (−12) is noticeably larger than the placebo’s (−4).
• The medians are similar to the means, suggesting the distributions aren’t wildly skewed.
• The interquartile range (IQR: 25th to 75th percentile) shows typical responses and whether there’s a lot of variability.

From here, researchers move into hypothesis tests and confidence intervals, but the summary statistics are the first sanity check: is there a clinically meaningful difference at all?

For real-world versions of this, look at trial summaries on ClinicalTrials.gov (a U.S. NIH resource: https://clinicaltrials.gov), where tables of means, standard deviations, and medians are standard.

Takeaway: In medicine, well-presented summary statistics are often the first filter before anyone cares about p-values.

Sports analytics: practical examples of summary statistics beyond just averages

Sports data is a goldmine for examples of summary statistics because every fan already thinks like an analyst.

Consider a basketball player’s scoring over a season:

• Mean points per game (PPG): 24.5
• Median PPG: 23
• Standard deviation: 7.2
• Minimum / maximum: 8 – 45
• Games with 30+ points: 18 out of 82

Two players can both average 24.5 PPG, but their summary statistics might tell very different stories:

• Player A: standard deviation 3.5, range 18–32 → consistent scorer.
• Player B: standard deviation 9.0, range 8–45 → streaky, boom-or-bust.

Coaches and analysts care about this. A stable performer might be more valuable in playoff scenarios, even with the same mean. This is a clean example of how standard deviation and range add nuance that the mean alone misses.

In baseball, the same logic applies to batting averages, on-base percentage, or exit velocity. Sites like MLB’s Statcast or NBA’s stats portals are basically giant collections of summary statistics: practical examples served to fans and teams.

Takeaway: In sports, the best examples of summary statistics distinguish consistency from volatility, not just overall performance.

Housing markets: examples include price distributions, not just averages

Real estate headlines love a single number: “Average home price hits $420,000.” But housing data is notoriously skewed, just like income.

Imagine a city’s recent home sales:

• Mean sale price: $520,000
• Median sale price: $410,000
• 25th percentile: $320,000
• 75th percentile: $650,000
• 90th percentile: $1,050,000
• Standard deviation: $260,000

Here, the median (\(410k) is far below the mean (\)520k), which signals that a relatively small number of high-end properties are dragging the average upward. The 90th percentile crossing $1 million shows a long upper tail.

Real estate analysts and agencies like the Federal Reserve and U.S. Census Bureau routinely publish median prices and percentiles because they’re more stable indicators of affordability. These are textbook examples of summary statistics: practical examples used in policy debates about housing affordability and zoning.

Takeaway: For housing, medians and percentiles are the workhorses; the mean is often more of a headline number than a realistic “typical” price.

Education and test scores: example of summary statistics for performance gaps

Standardized tests are another place where examples of summary statistics show up constantly. Suppose a school district wants to understand math performance for 8th graders.

They might look at:

• Mean score: 268
• Median score: 270
• Standard deviation: 32
• 25th / 75th percentiles: 245 / 292
• Percentage scoring above a proficiency cutoff (say 280): 42%

These numbers summarize thousands of individual scores into a quick profile of performance and spread. But the best examples of summary statistics come when you compare groups:

• School A: mean 280, SD 20
• School B: mean 260, SD 40

School A has higher average performance and lower variability. School B not only has a lower mean, but also a much wider spread—some students are doing very well, others are far behind.

Organizations like the National Center for Education Statistics (NCES) regularly publish tables of means, standard deviations, and percentiles for national assessments (see https://nces.ed.gov). These aren’t just academic; they guide funding, interventions, and accountability policies.

Takeaway: In education, summary statistics help identify both average performance and inequality in outcomes within and across schools.

Finance and investing: examples of summary statistics for risk and return

If you only look at average returns, you’re doing investing on “easy mode” and ignoring risk.

Suppose you’re comparing two stocks over the last 5 years, using monthly returns:

• Stock X

  • Mean monthly return: 0.9%
  • Standard deviation: 3%
  • Minimum / maximum monthly return: −7% / +11%

• Stock Y

  • Mean monthly return: 0.9%
  • Standard deviation: 7%
  • Minimum / maximum monthly return: −18% / +25%

Same mean return, very different risk profile. Stock Y is far more volatile. That standard deviation is not just a math detail; it’s the difference between “I can sleep at night” and “why is my portfolio down 15% this month?”

Analysts also use correlation as a summary statistic. For example, if Stock X and Stock Y have a correlation of 0.2, they don’t tend to move together much. That matters for diversification.

These are everyday examples of summary statistics: practical examples in finance—used in portfolio construction, risk management, and even regulatory filings.

Takeaway: In finance, summary statistics like mean, standard deviation, and correlation are the basic language of risk and return.

Public health: real examples of summary statistics during outbreaks

During COVID-19, the public got a crash course in summary statistics—sometimes well presented, sometimes not.

Public health agencies like the CDC (https://www.cdc.gov) routinely publish:

• Daily or weekly case counts and deaths (totals and rates per 100,000 people)
• Median age of hospitalized patients
• Percent of cases by vaccination status or age group
• Average length of hospital stay, with standard deviation or IQR

For example, a CDC report might say:

Among hospitalized adults, the median length of stay was 5 days (IQR 3–8 days).

That single sentence is a tidy example of summary statistics: it tells you the typical experience (median) and the middle spread (IQR), without getting lost in the full distribution of thousands of patients.

Public health also uses rates as summary statistics: cases per 100,000 people, deaths per 1,000 births, and so on. These make fair comparisons across regions with different population sizes.

Takeaway: In public health, the most useful examples of summary statistics make complex, large-scale data understandable for policymakers and the public.

Social media and digital products: examples include engagement and retention

Tech companies live on dashboards full of summary statistics: practical examples. Think of a product manager looking at last month’s app usage:

• Daily active users (DAU): mean 1.2 million
• Median session length: 6.5 minutes
• 90th percentile session length: 18 minutes
• Retention after 30 days: 34%
• Average number of sessions per user per day: 2.3

The median session length avoids being skewed by a few power users who stay on for an hour. The 90th percentile helps identify heavy users. Retention is a summary statistic over time: what percentage of users are still active after a fixed number of days.

When a new feature launches, product teams compare summary statistics before and after the change. If median session length rises from 6.5 to 7.8 minutes and 30-day retention nudges up from 34% to 38%, those are concrete, practical examples of summary statistics driving product decisions.

Takeaway: In digital products, summary statistics are the first line of feedback on whether users care about what you’ve built.

Key types of summary statistics behind these practical examples

At this point, you’ve seen a lot of examples of summary statistics: practical examples across domains. Under the hood, the same small toolkit keeps showing up.

Measures of center

• Mean (average): Sum of values divided by the number of values. Great when data are symmetric and outliers are rare.
• Median: Middle value when data are ordered. More stable than the mean when the distribution is skewed (income, housing prices) or contains outliers.
• Mode: Most frequent value. More useful for categorical data (most common diagnosis, most popular product size) than for continuous data.

Measures of spread

• Range: Max − min. Simple but sensitive to outliers.
• Variance and standard deviation: Capture how far values tend to be from the mean. Standard deviation has the same units as the original data, so it’s easier to interpret.
• Interquartile range (IQR): 75th percentile − 25th percentile. Focuses on the middle 50% of data, ignoring extremes.

Position and distribution shape

• Percentiles (including quartiles): Show where a value sits relative to the rest of the data (e.g., 90th percentile income).
• Skewness: Indicates whether the tail is heavier on the left or right. In practice, people often infer skew from comparing mean vs. median.
• Outliers: Extreme values that stand far from the bulk of the data.

Relationships between variables

• Correlation: Summarizes the strength and direction of a linear relationship between two variables (e.g., height and weight, study time and test scores).

The best examples of summary statistics don’t just report one number. They combine a few of these measures to give a richer, more honest picture of the data.

FAQ: common questions about examples of summary statistics

Q1. What are some everyday examples of summary statistics I already use without noticing?
Any time you hear an average, a median, or a percentage, you’re hearing a summary statistic. Common everyday examples include your GPA, your average gas mileage, the median home price in your city, or your average heart rate from a fitness tracker.

Q2. What’s a simple example of using summary statistics to compare two groups?
Think of comparing starting salaries for two college majors. If Major A has a mean salary of \(70,000 with a standard deviation of \)5,000, and Major B has a mean of \(68,000 with a standard deviation of \)18,000, you might prefer Major A’s more predictable outcomes even though the mean difference is small. That’s a clean example of how mean and standard deviation together inform a decision.

Q3. Why do analysts sometimes prefer the median over the mean in real examples?
In skewed data—like incomes, house prices, or hospital wait times—the mean can be distorted by a few extreme values. The median is more resistant to outliers, so it better reflects what a “typical” person experiences. Many of the examples of summary statistics: practical examples in income, housing, and healthcare rely on medians for that reason.

Q4. Where can I see real examples of summary statistics from reliable sources?
Several public sites publish high-quality data summaries:

• CDC for health and disease data: https://www.cdc.gov
• NIH / ClinicalTrials.gov for medical trial summaries: https://clinicaltrials.gov
• NCES for education and test score statistics: https://nces.ed.gov

These sites provide tables and reports full of means, medians, standard deviations, and percentiles—real examples you can cite in projects, papers, or reports.

Q5. How many summary statistics should I report in a project or paper?
Enough to tell an honest story without overwhelming your reader. For a continuous variable, a common pattern is: mean and standard deviation if the data are roughly symmetric, or median and IQR if the data are skewed. Add minimum/maximum or key percentiles when they help. The best examples of summary statistics are selective and purposeful, not a laundry list.