Every morning, the rooster crows, and shortly after, the sun rises. Do roosters cause the sunrise? Of course not. This is a classic example of one of the most important concepts in data analysis and strategic thinking: correlation does not imply causation.
What is Correlation?
Correlation is a statistical measure that describes how two variables move in relation to each other. If ice cream sales increase when the temperature rises, we can say there is a positive correlation between temperature and ice cream sales. It's a relationship.
What is Causation?
Causation means that a change in one variable *causes* a change in another. To establish causation, you need to prove that the change in the first variable is directly responsible for the change in the second, and that there isn't some other "lurking" variable at play.
In the ice cream example, it's not the ice cream sales that cause the temperature to rise, or vice versa. The lurking variable is the hot weather, which causes both an increase in temperature and an increase in people wanting to eat ice cream.
Why This Matters for Your Business or Policy
Confusing correlation with causation can lead to disastrous decisions:
- Bad Business Strategy: A hardware store might notice that sales of paint correlate with sales of roofing materials. They might then decide to bundle them together. But the real cause might be a boom in new home construction, which drives demand for both. A better strategy would be to market to new homeowners, not just bundle paint and roofing.
- Ineffective Public Policy: A city might observe that neighborhoods with more police patrols also have higher crime rates. A correlational view might suggest police cause crime. The causal view understands that police are deployed to areas *because* crime is already high. Reducing patrols would likely make the problem worse, not better.
The entire field of econometrics, which is at the core of what we do at Sterling Contractors, is dedicated to this challenge: building models and using techniques (like RCTs and quasi-experiments) to move beyond simple correlation and identify true causal relationships. This is how we provide our clients with the confidence to act.