Real-world examples of descriptive statistics in research analysis

Health research is packed with descriptive statistics because public agencies need to summarize data fast and clearly. Think of a CDC report on influenza or COVID-19: before any modeling, they start with counts, percentages, and measures of central tendency.

In a 2024 seasonal influenza surveillance summary from the Centers for Disease Control and Prevention (CDC), you’ll typically see:

• Counts and proportions: total number of lab-confirmed flu cases, hospitalizations, and deaths by age group.
• Rates per 100,000 people: hospitalization rate by age, which allows comparison across differently sized populations.
• Measures of central tendency: median age of hospitalized patients, average length of hospital stay.
• Spread: interquartile range (IQR) for age or length of stay, which shows how concentrated or spread out cases are.

Imagine a dataset of 10,000 hospitalized flu patients in the 2024–2025 season. Descriptive statistics might show:

• Median age: 67 years, IQR 55–78
• Mean length of stay: 4.3 days, standard deviation 2.1 days
• 62% of hospitalizations in patients 65 and older

Those are not just dry numbers. They immediately tell policymakers that older adults still carry most of the severe disease burden, guiding vaccine targeting and hospital capacity planning. This is a textbook example of descriptive statistics in research analysis: no hypothesis test yet, just a clear snapshot of who is getting sick and how badly.

Another health-focused example of descriptive statistics examples in research analysis appears in vaccine safety monitoring. The Vaccine Adverse Event Reporting System (VAERS), co-managed by the CDC and FDA, routinely summarizes reports by:

• Frequency of specific symptoms
• Median time from vaccination to symptom onset
• Distribution of reports by sex and age group

If 80% of mild fever reports occur within two days of vaccination, with a median duration of 24 hours, that descriptive summary gives clinicians a grounded way to counsel patients on what to expect.

For more real-world health data summaries, you can explore the CDC’s data pages at cdc.gov.

Education research: example of descriptive statistics in test score analysis

Education studies are another goldmine of descriptive statistics. Before researchers compare teaching methods or curricula, they summarize student performance.

Take a hypothetical 2024 study of 8th-grade math scores across 50 U.S. schools. Descriptive statistics examples in research analysis here might include:

• Mean math score per school
• Median score for urban vs. rural schools
• Standard deviation of scores within each school (how spread out performance is)
• Minimum and maximum scores to detect extreme outliers

Suppose the national sample shows:

• Mean math score: 255
• Median: 260
• Standard deviation: 45
• Range: 110 to 380

Right away, researchers can see that scores are fairly spread out. If one school has a mean of 330 with a tiny standard deviation of 10, it might signal a selective program or a data issue. That kind of outlier often appears first in descriptive tables.

In real-world education research, large-scale assessments like NAEP (often called the Nation’s Report Card) publish detailed descriptive statistics by state, race/ethnicity, and socioeconomic status. These summaries show score distributions before any causal claims are made. NAEP’s own reports, hosted at nces.ed.gov, are packed with examples of descriptive statistics examples in research analysis that policymakers actually read.

Business and marketing: real examples of descriptive statistics in customer data

Corporate analytics teams live on descriptive statistics. Before they build churn models or pricing strategies, they ask: what does the current customer base look like?

Picture an e-commerce company in 2025 analyzing 500,000 customer records. In a first-pass descriptive summary, analysts might compute:

• Average and median order value
• Distribution of orders per customer (mean, median, standard deviation)
• Percentage of customers by region
• Frequency of repeat purchases within 30, 60, and 90 days

Suppose the data show:

• Mean order value: $82
• Median order value: $49
• Standard deviation: $95
• 20% of customers account for 70% of total revenue

Those descriptive statistics scream skewed distribution—a small group of high spenders is pulling the mean up. This is one of the best examples of descriptive statistics examples in research analysis uncovering a business reality: most customers are light spenders, but a small segment is extremely valuable. That insight often leads to targeted loyalty programs or tiered pricing.

Descriptive statistics also highlight changes over time. If the median order value was \(60 last year and \)49 this year, but the mean stayed flat, analysts know that high-value orders increased while typical orders shrank. That nuance only appears when you compare mean vs. median and look at the shape of the distribution.

Public health surveys: examples include demographic breakdowns and weighted summaries

Large national surveys—like the U.S. National Health Interview Survey (NHIS) or the Behavioral Risk Factor Surveillance System (BRFSS)—are basically descriptive-statistics machines.

Researchers often report:

• Proportion of adults with a given chronic condition (e.g., diabetes)
• Mean BMI by age, sex, and region
• Median number of doctor visits per year
• Percentiles (10th, 25th, 75th, 90th) for variables like blood pressure or sleep duration

For instance, a 2023–2024 NHIS analysis might show that:

• 11% of adults report diagnosed diabetes
• Mean BMI among adults aged 45–64 is 30.2 kg/m²
• Median sleep duration is 7 hours, with an IQR of 6–8 hours

These descriptive numbers are not just background—they often are the main finding. Public health policy frequently rests on such summaries, especially when tracking trends over years. The National Institutes of Health (NIH) and its institutes, such as the National Heart, Lung, and Blood Institute at nhlbi.nih.gov, regularly publish reports where examples of descriptive statistics examples in research analysis form the backbone of policy briefs.

Social science and polling: examples of descriptive statistics in opinion research

Pollsters and social scientists use descriptive statistics to make sense of survey responses long before they model anything.

Consider a 2024 national poll on attitudes toward remote work, with 3,000 U.S. adults. Descriptive statistics might include:

• Percentage who prefer fully remote, hybrid, or fully in-person work
• Mean number of days per week respondents currently work from home
• Median age of those who prefer remote work vs. those who prefer in-person
• Cross-tabulated percentages by income, education, and region

You might see results like:

• 38% prefer hybrid, 34% fully remote, 28% fully in-person
• Mean days working from home: 2.6, standard deviation 1.8
• Median age of fully remote-preferring group: 41 years

Without any inferential statistics, these descriptive numbers already tell a clear story: hybrid work leads, fully remote is competitive, and preferences vary with age and possibly industry.

Polling organizations often publish detailed tables where examples include not just percentages, but also confidence intervals for those percentages. While the interval estimation is inferential, the core descriptive layer—counts and proportions—is what most readers focus on.

Clinical research: best examples of descriptive statistics in trial baselines

Open any randomized clinical trial in a medical journal and flip to the Baseline Characteristics table. That table is one of the best examples of descriptive statistics examples in research analysis you will ever see.

For each treatment arm, researchers report:

• Mean age ± standard deviation
• Percentage female, percentage with specific comorbidities
• Median follow-up time and IQR
• Distribution of disease severity at baseline

Imagine a 2025 oncology trial comparing two therapies in 600 patients. The baseline table might show:

• Mean age: 59.3 ± 10.8 years in the new-therapy group vs. 58.7 ± 11.1 in control
• 48% vs. 50% female
• 32% vs. 30% with diabetes
• Median tumor size: 3.1 cm (IQR 2.3–4.0) vs. 3.0 cm (IQR 2.2–4.1)

Those numbers are not decorative. They let readers judge whether randomization worked and whether the groups are comparable. If one arm had a much higher mean age or more severe disease at baseline, that would complicate interpretation of any treatment effect.

Websites like clinicaltrials.gov and major medical centers such as the Mayo Clinic at mayoclinic.org host trial summaries where descriptive statistics tables are front and center.

Data science workflow: where descriptive statistics fit in 2024–2025

Modern data science tools—Python’s pandas, R’s dplyr, or SQL-based analytics platforms—make it effortless to compute descriptive statistics. But the logic hasn’t changed: examples of descriptive statistics examples in research analysis still sit at the exploratory data analysis (EDA) stage.

In a 2025 machine learning project predicting hospital readmissions, a data scientist might:

• Summarize length of stay, age, and comorbidity counts with means, medians, and standard deviations.
• Look at readmission rates by hospital, ward, and day of week.
• Check for skewness and outliers using percentiles.

If they find that one hospital reports a mean length of stay of 0.5 days with a standard deviation of 0.1, while all others are around 4–6 days, that descriptive outlier suggests a data coding error. Catching that early can save the entire model from being distorted.

Increasingly, organizations also use automated EDA tools that generate descriptive statistics reports with:

• Summary tables for every variable
• Histograms and boxplots (visual forms of descriptive statistics)
• Correlation matrices

Even when the end goal is a sophisticated predictive model, these early descriptive summaries are the sanity check. They tell you if your data is remotely reasonable before you trust any algorithm.

Pulling it together: how to read examples of descriptive statistics in research analysis

Across all these domains, the pattern is the same. When you look at real examples of descriptive statistics examples in research analysis, you’re usually seeing answers to a handful of basic questions:

• What is typical?
  Means and medians answer this.

• How much variation is there?
  Standard deviations, ranges, and IQRs answer this.

• How is the data distributed across groups?
  Percentages and cross-tabulations answer this.

• Are there outliers or weird patterns?
  Minimums, maximums, and percentiles help spot these.

The best examples of descriptive statistics are not just long tables—they’re interpreted tables. Researchers don’t stop at “the mean is 255”; they ask, “Is 255 high or low compared with last year, other groups, or policy targets?”

When you read a paper or report, try this quick checklist:

• Compare mean vs. median to check for skew.
• Look for measures of spread; a mean without a standard deviation or IQR is only half the story.
• Scan group comparisons in baseline tables for imbalances.
• Watch for tiny subgroups with big percentage swings; descriptive statistics can exaggerate noise if n is small.

Once you start looking for them, you’ll see examples of descriptive statistics examples in research analysis everywhere—from a CDC fact sheet to a quarterly business review. They’re the quiet backbone of data storytelling.

FAQ: common questions about descriptive statistics examples

Q1. What are some simple examples of descriptive statistics in everyday research?
A health survey reporting the average number of doctor visits per year, a school district summarizing median test scores by grade, or a company showing the percentage of customers who churned last quarter are all simple examples of descriptive statistics in research analysis.

Q2. Can you give an example of descriptive statistics vs. inferential statistics?
An example of descriptive statistics is reporting that, in a sample of 2,000 adults, 27% smoke cigarettes and the mean age is 44. An inferential step would be using that sample to estimate the smoking rate in the entire population with a confidence interval or to test whether the rate differs from a previous year.

Q3. Why do research papers always include tables of descriptive statistics?
Those tables let readers judge data quality, spot outliers, and understand the sample before they interpret any models or p-values. Without examples of descriptive statistics examples in research analysis, it’s almost impossible to know whether later conclusions are grounded in a sensible dataset.

Q4. What are the best examples of descriptive statistics to report for skewed data?
For skewed variables like income, hospital length of stay, or number of social media followers, the median and IQR are usually better examples of descriptive statistics than the mean and standard deviation. The median is less distorted by extreme values.

Q5. Where can I see real examples of descriptive statistics in published research?
You can browse health reports at the CDC (cdc.gov), education data at the National Center for Education Statistics (nces.ed.gov), and medical trial summaries at clinicaltrials.gov. Their tables and summaries are full of real examples of descriptive statistics examples in research analysis that professionals actually use.