The best examples of chi-square test examples in healthcare studies

Real-world examples of chi-square test examples in healthcare studies

Let’s start where most people get interested: actual data questions that hospitals, public health agencies, and clinical researchers have to answer. These examples of chi-square test examples in healthcare studies all revolve around the same basic idea: are two categorical variables related, or not?

In healthcare, those variables are often things like:

• Treatment group (drug vs placebo)
• Outcome (improved vs not improved)
• Side effect (yes vs no)
• Risk factor (smoker vs non-smoker)
• Demographic category (age group, sex, race/ethnicity)

The chi-square test compares observed counts in a contingency table to expected counts if there were no relationship. When the difference is big enough, you get a small p-value and evidence of an association.

Classic clinical trial example of chi-square test: treatment vs placebo

One of the most common examples of chi-square test examples in healthcare studies is a randomized controlled trial where the outcome is categorical.

Imagine a 2024 hypertension trial with 600 patients:

• 300 get a new blood pressure drug
• 300 get a placebo
• Outcome after 6 months: controlled vs uncontrolled blood pressure

The data might look like this:

The chi-square test of independence asks: Is blood pressure control independent of treatment group?

• Null hypothesis (H₀): Treatment and BP control are independent.
• Alternative (H₁): There is an association between treatment and BP control.

If the chi-square statistic is large and the p-value is under the chosen alpha (often 0.05), the team concludes that the new drug is associated with better BP control.

This is exactly the structure used in countless cardiovascular and diabetes trials indexed in PubMed and summarized by agencies like the National Institutes of Health.

Vaccine safety and side effects: another core example of chi-square test

Vaccine studies provide some of the best examples of chi-square test examples in healthcare studies because they typically track yes/no side effects by vaccine type or dose.

Consider a simplified COVID-19 booster safety study in 2024, comparing two booster formulations:

Researchers use a chi-square test to evaluate whether the proportion of people reporting side effects differs between Booster A and Booster B.

Why not just compare percentages with a t-test? Because the data are categorical counts, not continuous measurements. The chi-square test is built for this exact setup.

Organizations like the CDC routinely use chi-square tests when summarizing adverse event rates across groups.

Hospital readmissions: chi-square test in quality improvement

Hospital quality teams love two-by-two tables. One very common example of chi-square test in healthcare is evaluating whether an intervention reduces 30-day readmissions.

Suppose a hospital implements a new discharge education program for heart failure patients and compares readmission status before and after implementation:

The chi-square test checks whether the distribution of readmission vs no readmission differs between the two periods.

• If the p-value is small, the hospital has statistical evidence that readmission rates changed.
• That becomes one data point (not the only one) in deciding whether to keep or expand the program.

This type of analysis shows up in readmission reduction efforts tied to U.S. policy initiatives and monitored by agencies like the Centers for Medicare & Medicaid Services.

Health disparities: examples of chi-square test in public health studies

Some of the most important examples of chi-square test examples in healthcare studies come from health equity research, where the question is whether outcomes differ across demographic groups.

Imagine a 2025 public health study on colorectal cancer screening participation by race/ethnicity (simplified):

A chi-square test of independence evaluates whether screening status is associated with race/ethnicity.

• A significant result suggests that screening rates differ by group.
• Researchers then move to effect size measures (like Cramér’s V) and multivariable models, but the chi-square test is often the first signal that disparities exist.

You see this pattern regularly in surveillance reports from agencies like the CDC’s National Center for Health Statistics.

Lifestyle risk factors and disease: smoking, obesity, and more

Epidemiologists use chi-square tests constantly to explore associations between lifestyle risk factors and disease outcomes. These are some of the best examples because the logic is very intuitive.

Smoking and chronic obstructive pulmonary disease (COPD)

Suppose a 2024 clinic-based study tracks COPD diagnosis by smoking status:

The chi-square test evaluates whether COPD status is independent of smoking category.

• If the test is significant, it supports the association between smoking and COPD.
• Researchers often follow up with relative risks or odds ratios, but the chi-square test provides the first formal check.

Obesity and type 2 diabetes

Another very common example of chi-square test in healthcare compares BMI category with diabetes diagnosis. Data from surveys like the National Health and Nutrition Examination Survey (NHANES) are often summarized this way.

Again, the structure is the same: a contingency table with BMI categories on one axis and diabetes (yes/no) on the other, followed by a chi-square test.

Chi-square goodness-of-fit in healthcare: do observed cases match expectations?

Most people think of chi-square tests for comparing two variables, but the goodness-of-fit version shows up in healthcare surveillance.

Example: Monitoring disease cases against expected seasonal patterns

Public health departments often expect certain seasonal patterns of diseases like influenza. Suppose a city expects flu cases across four winter months to follow a known distribution based on historical data:

• December: 20%
• January: 35%
• February: 30%
• March: 15%

In winter 2024–2025, they observe the following counts:

A chi-square goodness-of-fit test checks whether the observed monthly pattern differs from the expected one.

• If the pattern is statistically different, that might trigger a closer look for changes in virus strain, vaccination coverage, or reporting practices.

This is one of the less flashy but very real examples of chi-square test examples in healthcare studies, especially in epidemiology and infection control.

Chi-square test for trend: ordered categories in modern studies

When categories have a natural order (for example, age brackets or dose levels), researchers often use the chi-square test for trend (also called the Cochran–Armitage trend test) rather than a generic chi-square test of independence.

Example: Dose-response in a phase II oncology trial

Imagine a cancer trial testing three dose levels of a new oral drug, with response coded as yes/no:

There’s a clear ordering to the dose levels. The chi-square test for trend asks whether there is a linear trend in response rates across doses.

• This is widely used in 2020s oncology and cardiology trials when doses are escalated.
• The trend test is more powerful than a generic chi-square test when a monotonic pattern is expected.

Again, this sits alongside other analyses, but it’s a very standard example of chi-square test in healthcare.

Diagnostic test performance: sensitivity, specificity, and chi-square

Diagnostic accuracy studies provide another fertile ground for examples of chi-square test examples in healthcare studies.

Example: Comparing two rapid tests for influenza

Suppose a lab compares Test A and Test B against a gold standard PCR for flu infection:

Researchers might structure this as two separate 2×2 tables (one for each test) or a larger table, then use chi-square tests to compare sensitivity and specificity between tests.

• For example, a chi-square test can compare the proportion of PCR-positive patients correctly identified by Test A vs Test B.
• This helps labs decide which test to adopt, especially during high-demand periods like flu season.

Organizations like Mayo Clinic often publish performance comparisons where chi-square tests underpin the p-values.

Electronic health records and big data: chi-square at scale

With the growth of electronic health records (EHRs) and large-scale databases, chi-square tests are now run on millions of patient records.

Example: Medication safety signal detection

Pharmacovigilance teams may scan EHRs to see whether a new medication is associated with a higher-than-expected rate of a specific adverse event.

• Patients are categorized as exposed vs not exposed to the medication.
• The adverse event is coded as present vs absent.
• A chi-square test checks whether the event is disproportionately common among exposed patients.

Because the sample sizes in 2024–2025 datasets are huge, p-values can be tiny even for small differences. That’s why researchers pair chi-square results with effect sizes and clinical judgment rather than blindly chasing significance.

This large-scale use of chi-square is increasingly common in collaborations between academic medical centers and data networks like PCORnet and the NIH’s All of Us Research Program.

Interpreting chi-square results responsibly in healthcare

Across all these examples of chi-square test examples in healthcare studies, a few interpretation points keep coming up:

• Association, not causation: A significant chi-square test tells you there is a relationship between variables, not that one causes the other.
• Sample size matters: With very large sample sizes (think EHR data), tiny differences can be statistically significant but clinically trivial.
• Expected cell counts: The usual rule of thumb is that expected counts in each cell should be at least about 5. If not, researchers often switch to Fisher’s exact test.
• Effect sizes: Measures like Cramér’s V or phi coefficient help quantify how strong the association is, which is more informative than p-values alone.

If you look at modern clinical papers indexed by PubMed or guidelines summarized by Harvard Medical School, you’ll see chi-square tests reported alongside these context pieces rather than in isolation.

FAQ: common questions about chi-square test examples in healthcare

What are some typical examples of chi-square test in healthcare research?

Typical examples include comparing treatment vs placebo response rates, side effect frequencies across drug groups, hospital readmission rates before and after an intervention, disease prevalence across demographic groups, and screening participation rates by insurance status or region. These are all examples of chi-square test examples in healthcare studies where both variables are categorical.

Can you give an example of using chi-square test in a hospital setting?

Yes. A hospital might use a chi-square test to compare falls on the ward (yes/no) before and after a new fall-prevention protocol. If the distribution of “fall vs no fall” changes significantly between the two time periods, the protocol may be considered effective, pending other checks.

When should researchers avoid using the chi-square test in healthcare studies?

Researchers should avoid chi-square tests when expected cell counts are very small, when the outcome is continuous (like blood pressure in mm Hg), or when observations are not independent (for example, repeated measures on the same patient without proper modeling). In those cases, alternatives like Fisher’s exact test, t-tests, or mixed models are more appropriate.

Are chi-square tests still relevant in 2024–2025 with advanced machine learning methods?

Absolutely. Machine learning models are powerful, but chi-square tests remain a workhorse for quick, interpretable association checks in healthcare. They are transparent, easy to report, and widely understood by clinicians, which makes them ideal for early-phase analysis, quality improvement work, and regulatory reporting.

Where can I see real examples of chi-square test results in published healthcare studies?

You can browse open-access articles on PubMed Central, CDC surveillance reports, and clinical trial summaries on NIH and major academic medical center websites. Many of these reports show contingency tables and p-values from chi-square tests in the methods or results sections.