Short answer: A correlation shows that two variables change together; it does not show that one causes the other. The relationship may be coincidence, a shared third factor, a time trend, reverse causation, or the result of searching enough data until an impressive match appears.
The examples below include documented pairings such as margarine consumption and Maine’s divorce rate, plus recurring patterns that make plausible claims misleading.
What is a spurious correlation?
A spurious correlation is an observed statistical association that does not represent the causal relationship its appearance suggests. The correlation can be mathematically real: the two series may move together in the available data. What is missing is a demonstrated mechanism showing that changing X changes the probability distribution of Y.
Correlation coefficients summarize the strength and direction of an association. They do not identify the cause, establish the direction of influence, or rule out other explanations.
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15 examples of spurious or misleading correlations
1. Margarine consumption and Maine divorce rates
Tyler Vigen’s famous comparison pairs annual per-capita margarine consumption in the United States with Maine’s annual divorce rate. The University of Illinois Pressbooks primer reports a correlation of r = 0.99. That striking coefficient does not make margarine a cause of divorce; it illustrates how closely unrelated-looking time series can track.
2. U.S. science spending and deaths by hanging, strangulation, and suffocation
An academic epidemiology text presents U.S. science spending alongside deaths classified as hanging, strangulation, and suffocation. Similar movement over time is not a credible causal explanation. A shared trend, changing population conditions, or random alignment can produce the visual pattern.
3. Swimming-pool deaths and Nicolas Cage movies
The Urban Institute uses swimming-pool deaths and the number of Nicolas Cage movies as an absurd example. A high association between the series would not mean movie releases cause drownings, or drownings cause movie releases. It demonstrates that a large coefficient can coexist with an implausible mechanism.
4. Ice-cream eating and sunburn
Ice-cream consumption and sunburn can rise together because people spend more time outdoors in warm, sunny weather. Outdoor exposure is a common cause of both observations. Treating ice cream as the cause of sunburn mistakes confounding for causation.
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5. Chocolate consumption and Nobel laureates per capita
A reported cross-country association between chocolate consumption and Nobel laureates per capita is sometimes framed as evidence that chocolate improves cognitive ability. Countries differ in wealth, education, research institutions, diet, and population structure. Those factors could confound the relationship, so the correlation alone cannot support the causal claim.
6. Immigration and local literacy rates
The Urban Institute uses immigration and local literacy as a plausible-looking relationship that needs more than a chart. Population sorting can place immigrants and literacy resources unevenly across regions. The observed association may reflect where people move, who is counted, local schooling, or other characteristics rather than immigration changing literacy.
7. Car ownership among low-income families and moving to better neighborhoods
If low-income families with cars are more likely to move to better neighborhoods, the car may appear to cause the move. An alternative is that families able to afford a car also have savings, stable employment, credit access, or other resources that make moving possible. The car is then a marker of underlying resources, not the sole cause.
8. Two unrelated series that both trend upward
Population growth, prices, technology adoption, and many other measures tend to rise over time. Correlating any two upward-trending series can produce a strong coefficient even when neither affects the other. Detrending, examining changes rather than levels, and testing a prespecified mechanism are necessary before making a causal claim.
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9. Two unrelated series that both trend downward
The same problem occurs with declining series. Improvements in one measure may coincide with reductions in another simply because both reflect a long-term trend, policy era, or demographic change. Moving in the same direction is not evidence of a causal pathway.
10. The highest correlation found among many candidate pairs
Suppose an analyst compares thousands of variables and reports only the pair with the largest coefficient. Some extreme matches are expected by chance. This is a multiple-testing and selection problem: the reported result is conditional on having searched for a striking relationship, not on having tested a theory in advance.
11. Two variables linked by a shared third factor
A third variable can influence both measured variables. Weather, age, income, policy, geography, and season are common examples of potential confounders. Adjusting for a confounder can substantially change an association, although statistical adjustment itself depends on having measured and modeled the relevant factors correctly.
12. A relationship with the direction reversed
Cross-sectional data often measure exposure and outcome at one point in time. They may not show whether X preceded Y. For example, better health could increase employment, while employment could also improve health. Without temporal ordering or an appropriate design, the proposed direction remains uncertain.
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13. A plausible association affected by confounding
A relationship can sound sensible and still be noncausal. The immigration-literacy and car-neighborhood examples are useful precisely because the proposed explanation feels reasonable. Ask what else differs between the groups and whether those differences could account for the outcome.
14. A dramatic coefficient shown without the selection process
A chart can draw attention to a coefficient while omitting how the variables, years, and comparison pairs were chosen. Selection decisions affect interpretation. A result found after extensive searching should be treated as exploratory unless it is confirmed on new data or tested under a prespecified analysis.
15. A mathematically correct correlation with a misleading story
Every number in a chart can be calculated correctly and still be narratively misleading. The error occurs when a descriptive association is presented as an explanation without evidence about mechanism, timing, alternatives, or intervention.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why do unrelated things sometimes seem correlated?
Coincidence and multiple testing
There are vastly more possible pairs of variables than most readers see. If enough combinations are examined, some will line up unusually well by chance. Vigen describes his project as playful and intentionally misleading; the January 2024 update added 25,000 variables, increasing the number of possible comparisons. A selected chart is therefore not equivalent to a randomly chosen hypothesis test.
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Common causes and confounding
A common cause can move both variables together. Ice-cream eating and sunburn are the accessible example: outdoor time and sunny weather affect both. In observational studies, confounding can remain even when the association fits an intuitive story.
Shared time trends
Time-series data can create high correlations when both measures drift upward or downward. Inspect the dates, the scale, seasonality, structural breaks, and whether the result survives analysis of changes or deviations from trend.
Reverse causation
The outcome may influence the supposed cause, or each may influence the other. A one-time snapshot rarely resolves that direction. Longitudinal measurements, natural experiments, randomized interventions, or other designs that establish temporal order are more informative.
Does correlation mean causation?
No. Correlation is evidence that variables are associated in the observed data; it is not, by itself, evidence that changing one will change the other. A causal conclusion needs a defensible mechanism and a design or analysis that distinguishes that mechanism from coincidence, confounding, time trends, and reverse causation.
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The academic definition of causation is intervention-based: if intervening on X would change the probability distribution of Y, X has a causal effect on Y. Merely observing X and Y together does not perform that intervention.
How to evaluate a correlation before accepting the claim
- Check the measurement. Identify exactly how each variable was defined, who was measured, and which years or regions were included.
- Ask what was selected. Was the pair, time window, subgroup, or outcome chosen before analysis, or selected because it looked impressive?
- Look for a common cause. Consider weather, season, age, income, geography, policy, population changes, and other variables that could affect both measures.
- Establish the timeline. Verify that the proposed cause precedes the outcome and consider whether the direction could be reversed.
- Test robustness. Check whether the association survives alternative specifications, additional data, different periods, and correction for repeated testing.
- Prefer intervention-based evidence. Randomized experiments, credible natural experiments, and carefully justified causal designs provide stronger evidence than a single observational coefficient.
How common is causal language in observational research?
A 2026 Nature Human Behaviour study reported that 46.3% of the cross-sectional studies in its defined corpus and classification used causal language. That figure is not a universal rate for all research. It highlights why readers should distinguish an observational association from a claim about what would happen under intervention; cross-sectional, non-experimental designs are especially vulnerable to confounding and reverse causality.
How to read Vigen’s charts responsibly
Vigen’s project appeared on the web in 2014, followed by a book edition in 2015. The charts are intended as playful demonstrations, not causal studies. Use the “data details” information attached to a chart to identify its underlying sources, and remember that processing choices can occur between raw data and the final graphic. A chart can be useful for learning statistical skepticism without being evidence that either variable causes the other.
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