Posted by Dmytro Dodenko
A strong correlation between two datasets (meaning they are closely related) does not necessarily imply a causal relationship (meaning one causes the other). Let’s consider a classic statistical example that perfectly illustrates the difference between correlation and causation.
Imagine a sunny coastal resort at the height of the summer season. The air is filled with laughter and the scent of salt water and sunscreen. Amidst this idyll, inside a cool city council office, a young analyst was hard at work. He believed that the city’s spreadsheets held the key to a safer summer for everyone.
1. A Sunny Day and Strange Statistics
Day after day, the analyst reviewed summer reports: tourist traffic, local café revenue, and police reports. It was routine work that involved searching for patterns within columns of numbers and charts. But suddenly, something strange caught his eye, making him set aside his coffee cup and lean closer to the monitor. He had stumbled upon statistics that seemed completely unrelated yet surprisingly synchronized.
2. The Discovery: An Unexpected Connection
Guided by intuition, the analyst decided to compare two seemingly disparate metrics: daily ice cream sales at beach kiosks and the number of lifeguard rescues in the water. He plotted a simple chart to visualize the data from the past few weeks.
The result stunned him. A clear, almost perfect relationship appeared on the screen. Whenever the ice cream sales curve crept up, the curve for water rescue operations followed almost perfectly. On days with low ice cream sales, the waters were calm. But on days with record sales, the lifeguards were working non-stop.
This strong positive correlation seemed too obvious to ignore. He was convinced he had found the cause – a discovery that could save lives.
3. A False Conclusion: Is Ice Cream Really Dangerous?
Carried away by his discovery, the analyst began to draw a hasty but, as it seemed to him, logical conclusion. If the rise in one metric coincides with the rise in another, there must be a causal link. He started to speculate: perhaps the sugar in the ice cream causes cramps in swimmers? Or maybe the cold dessert somehow dulls people’s alertness in the water?
His hypothesis, though absurd, was based on real data. He even prepared a draft report for his manager, proposing a radical solution: “Perhaps we should restrict ice cream sales on the beach? The data clearly shows: more ice cream equals more accidents in the water!”
Fortunately, before sending the report, he decided to think through his conclusions one more time. Something about this logic felt wrong. Could the connection really be that simple and direct?
4. The Moment of Truth: The Third Factor
The analyst looked up from his monitor and glanced out the window. The sun was blinding, the beach was packed with people, and a family holding ice cream cones was walking past his office. Suddenly, everything clicked. He had missed the most crucial element – one that was invisible on his charts. This hidden factor was influencing both ice cream sales and people’s behavior on the beach simultaneously. That factor was the hot sunny weather.
The true causal chain was much more logical:
Hot weather causes two independent events:
- People buy significantly more ice cream to cool down.
- People flock to the sea to swim. The more people in the water, the higher the probability of accidents and, consequently, the higher the number of lifeguard rescues.
Ice cream was not the cause of the danger. It was just another consequence of the heat, just like the number of swimmers. The analyst realized he had confused a simple connection with a cause.
5. The Main Lesson: Correlation is Not Causation
This story is a classic example of one of the most common traps in data analysis: confusing correlation with causation.
- Correlation is a statistical relationship showing that two metrics change synchronously (they rise together, fall together, or move in opposite directions).
- Causation (cause-and-effect relationship) means that a change in one metric directly causes a change in the other.
Let’s compare these concepts using our story:
| Concept | Explanation using the example |
| Correlation | Ice cream sales and lifeguard rescues rise at the same time. This is just a connection. |
| Causation | The false assumption that eating ice cream causes water accidents. |
A strong correlation is not a final conclusion, but merely a signal for deeper analysis. It hints at where to look, but it doesn’t give the final answer.
How to Think Critically About Data
The ice cream story is both a classic example and a warning against hasty conclusions based on superficial connections between numbers. To avoid falling into this trap, always remember three simple rules:
- Always look for the third factor. Ask yourself: could there be another hidden cause influencing both metrics simultaneously? In our case, it was the weather.
- Remember the difference. Correlation only shows that two things move together. Treat it as a clue, not a fact.
- Ask the right questions. Instead of asking “Does A cause B?”, ask “What else could explain this connection?”. This simple question forces you to think broader and look for the real reasons rather than obvious but false answers.
