Correlation vs Causation: 10 Hilarious Examples That Prove the Point

Correlation vs. Causation: 10 Hilarious Examples That Prove the Point

Every time I explain correlation versus causation to my university students, I watch the same thing happen: their eyes glaze over during the textbook definition, then light up the moment I show them something absurd. Like the fact that per capita cheese consumption in the United States correlates almost perfectly with the number of people who died by becoming tangled in their bedsheets. The correlation coefficient? A staggering 0.947 (Vigen, 2015). Nobody in their right mind thinks eating brie is a mortal threat, but the numbers say something is going on.

Related: cognitive biases guide

Here’s the thing most people miss about this topic.

This is exactly why I love ridiculous examples. As someone with ADHD who also happens to teach Earth Science at Seoul National University, I’ve learned that the brain locks onto funny, surprising information far more effectively than dry definitions. And for knowledge workers — people who make decisions based on data every single day — understanding the difference between correlation and causation isn’t just academically interesting. It can determine whether your team invests in the right strategy or wastes six months chasing a statistical ghost. [2]

Let’s get into it.

What’s the Actual Difference?

Before the fun stuff, a quick grounding. A correlation is a statistical relationship between two variables — when one goes up or down, the other tends to follow. Causation means one variable actually produces a change in the other. The classic logical error — assuming that because two things happen together, one must cause the other — has a name: post hoc ergo propter hoc, Latin for “after this, therefore because of this.”

The reason this error persists even among smart, data-literate professionals is that causation always comes with correlation. If smoking causes cancer, smoking and cancer rates will be correlated. The problem is that correlation is common, cheap to find, and doesn’t require understanding anything about mechanisms. Causation is rare, hard to establish, and requires either controlled experiments or very careful observational study design (Pearl & Mackenzie, 2018). Our brains, wired to spot patterns for survival, treat correlation as “good enough” evidence — and that instinct routinely leads us astray.

Ten Glorious Examples of Correlation Gone Wrong

1. Nicolas Cage Films and Swimming Pool Drownings

This one has become almost legendary in statistics education. Between 1999 and 2009, the number of people who drowned in swimming pools correlated with the number of Nicolas Cage films released that year, with a correlation of 0.666 (Vigen, 2015). The obvious implication — that watching Con Air causes people to leap into pools — is not supported by any known mechanism. What’s actually happening is a phenomenon called a spurious correlation: two variables that happen to move in similar patterns over time purely by chance, with no third variable linking them and no causal arrow between them.

For knowledge workers, the lesson here is sobering. If you have enough metrics in your dashboard — page views, conversion rates, employee satisfaction scores, weather data, stock prices — some of them will correlate with your sales numbers by pure chance. The more variables you track, the more spurious correlations you’ll find. This is the multiple comparisons problem, and it has torpedoed countless A/B tests and quarterly business reviews.

2. Ice Cream Sales and Shark Attacks

Both rise in summer. Therefore, ice cream causes shark attacks. Or sharks, sensing the presence of frozen dairy products, become aggressive. Neither is true, of course. There’s a third variable — hot weather — that drives people to buy ice cream and drives people into the ocean, where sharks occasionally exist. This is called a confounding variable (also known as a lurking variable), and it’s arguably the most dangerous type of correlation trap because the relationship feels so real.

The confounding variable problem is everywhere in workplace analytics. If remote workers show higher productivity scores and higher job satisfaction, it’s tempting to say one causes the other. But both might be caused by a third factor: working on projects they find meaningful. Intervening on satisfaction without addressing project quality won’t move productivity at all.

3. Organic Food Sales and Autism Diagnoses

Both have risen dramatically since the early 2000s. The correlation is genuinely strong. The mechanism? Nonexistent. What’s actually happening is that both trends have independent explanations: organic food sales rose because of changing consumer preferences and marketing; autism diagnosis rates rose primarily because of broadened diagnostic criteria and increased awareness and screening (Lundström et al., 2015). Conflating these trends has real-world consequences — it feeds pseudoscientific claims that have caused genuine harm to families seeking accurate information about autism.

4. The Drowning Rate in Alabama vs. Per Capita Mozzarella Consumption

Back to Tyler Vigen’s spectacular dataset. Per capita mozzarella cheese consumption in the US correlates with civil engineering doctorates awarded, at 0.958. Drowning rates in Alabama correlate with per capita cheese consumption at equally absurd levels. These examples work because cheese consumption and various societal metrics all tend to rise together during periods of economic prosperity, creating what statisticians call time-series confounding. When everything is trending upward together, almost everything correlates with everything else, and none of it means anything causal. [1]

5. Storks and Birth Rates in Europe

This one is so good it became a published academic paper, written specifically to illustrate spurious correlation. Across European countries, the number of breeding stork pairs correlates with national birth rates (Matthews, 2000). Larger countries have more rural land, more storks, and more babies — all driven by the confound of country size and population. The paper’s author deliberately chose the example to demonstrate that statistical significance alone, without theoretical reasoning, is meaningless. The stork delivers babies. The p-value says so. This is why domain knowledge matters as much as statistical fluency.

6. Pirates and Global Warming

This is the beloved example from the Church of the Flying Spaghetti Monster’s satirical open letter, but the logic it skewers is real. As the global number of pirates has declined since the 1800s, global average temperatures have risen. Therefore, pirates prevent global warming. The argument is absurd, but it’s structurally identical to many real claims made in op-eds and board presentations. The decline in pirates and the rise in temperature are both long-running trends with completely independent causes. Two things moving in opposite directions over time does not establish that one is reversing the other.

7. Shoe Size and Reading Ability in Children

Researchers have found that among children, larger shoe size predicts better reading ability. Does this mean we should buy our kids bigger shoes? No — older children have bigger feet and more developed reading skills. Age is the confound. This example is particularly useful for HR professionals and educators who run statistical analyses on employee or student populations without controlling for obvious background variables. When you don’t account for experience level, tenure, or age, you’ll find correlations everywhere that evaporate the moment you stratify your data properly.

8. Countries with More TV Sets Have Lower Birth Rates

This one circulated seriously for a while, with some commentators earnestly suggesting that television watching suppresses fertility. The actual explanation is simpler: television ownership is a proxy for wealth and development, and wealthier, more developed nations tend to have lower birth rates for a complex set of social, economic, and educational reasons that have nothing to do with watching television (though access to information and education conveyed through media may play a partial role). The TV set is a marker, not a cause. Confusing markers with mechanisms leads to policy decisions that do nothing. [3]

9. The More Firefighters Respond to a Fire, the More Damage There Is

This one trips people up because it sounds like a reasonable argument against firefighters. Dispatch data genuinely shows that larger fires get more firefighters and cause more damage. But the causal arrow runs in the opposite direction: larger fires cause both more damage and require more firefighters. The fire size is the common cause. This is called reverse causation combined with a common cause structure, and it’s a trap that shows up in business analytics constantly. More customer support tickets correlate with more support staff — not because support staff generates tickets, but because growing customer bases generate both.

10. Sleeping with Your Shoes On Causes Headaches

Studies of bar patrons found that people who sleep with their shoes on frequently wake up with headaches. Clearly, shoes on the pillow are the culprit. Except, obviously, both sleeping with your shoes on and waking up with a headache are caused by a third variable: having consumed a large amount of alcohol the previous evening. This is the classic confound example from introductory statistics, and it’s funny precisely because the intervention it implies — removing your shoes before bed — is so wildly beside the point. In organizational settings, this pattern looks like blaming meeting fatigue on a particular project management tool when both are driven by company-wide overcommitment.

Why Smart People Still Fall for This

Knowledge workers aged 25–45 are not naive. Most of you have encountered the phrase “correlation is not causation” so many times it’s practically a LinkedIn cliché. And yet the reasoning error persists in real strategic decisions, marketing interpretations, and performance reviews. Why?

Part of the answer is cognitive load. When you’re under deadline pressure, running on insufficient sleep, and looking at a chart that shows two lines moving together, your brain’s pattern-matching system fires before your critical evaluation system has time to ask “but why?” This is System 1 thinking dominating System 2, to use Kahneman’s framework — fast intuition overriding slow analysis (Kahneman, 2011).

Another part is incentive structure. If a correlation supports the conclusion you were hoping to reach, there’s psychological pressure to accept it. This is motivated reasoning in its data-drenched modern form. The chart becomes evidence not because it was rigorously evaluated but because it was convenient.

And finally, there’s the very real fact that establishing causation is genuinely difficult. Randomized controlled trials are the gold standard but are often impossible in business or policy contexts. Quasi-experimental methods — difference-in-differences, instrumental variables, regression discontinuity designs — require statistical expertise most teams don’t have readily available. So people default to correlation and call it causation, consciously or not.

A Practical Framework for Thinking More Clearly

When you encounter a correlation that’s being used to justify a decision, run through these questions quickly. First: Is there a plausible mechanism? Not just “can I imagine a story,” but is there a known physical, psychological, or economic process that would link these variables? No mechanism, no causation claim. Second: Could a third variable explain both? Think about what background factors might be driving both trends — time, geography, wealth, population size, and organizational maturity are common culprits. Third: Could the causation be reversed? Check whether the implied direction of cause and effect makes sense, or whether the data is equally consistent with the opposite arrow.

These three questions won’t make you a causal inference expert, but they’ll catch the most obvious errors before they become expensive strategic decisions. Pearl and Mackenzie (2018) describe this kind of structured causal thinking as building a do-calculus — asking not just “what do I observe when X changes?” but “what would happen if I intervened to change X?” That distinction between observation and intervention is the heart of causal reasoning, and it’s learnable.

Sound familiar?

The Real Cost of Getting This Wrong

The cheese-and-bedsheet correlation is funny. The real-world versions aren’t always. When a pharmaceutical company misreads observational data and concludes that a supplement causes improved outcomes, patients make decisions based on that conclusion. When a manager notices that employees who attend optional training sessions perform better and mandates the training for everyone, forgetting that motivated employees seek out training and perform better — both driven by motivation — the mandatory training costs money and time and changes nothing. When a government notices that countries with more CCTV cameras have lower crime rates and installs cameras everywhere, without controlling for the fact that both cameras and crime prevention funding flow to the same wealthy jurisdictions, public money evaporates.

These are not abstract failures. They are the predictable result of treating correlation as causation in high-stakes contexts. The Nicolas Cage pool-drowning statistic is harmless. The same logical structure applied to medical data, business investment, or public policy is not.

The good news is that ridiculous examples like the ones above are genuinely useful — not just as entertainment, but as cognitive vaccines. Once your brain has seen the stork correlation and laughed at it, it becomes slightly harder to accept the next plausible-sounding correlation without asking the right questions first. That’s not a small thing. Building good epistemic habits on memorable, funny examples is a pedagogical strategy with real evidence behind it, and it’s one I use every semester because it works.

In my experience, the biggest mistake people make is

Related Reading

• The Zeigarnik Effect: Why Unfinished Tasks Haunt Your Brain
• Second-Order Thinking: How to See Consequences Others Miss
• Weekly Review Ritual: The 30-Minute Habit That 10x Your Productivity

Last updated: 2026-03-31

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Sources

• LessWrong
• Behavioral Science & Policy Association
• Decision Science News