Which concept cautions that correlation does not equal causation?

• A. Misleading Data
• B. Correlation vs. Causation
• C. Evidence
• D. Authority

Answer: (B)

The main idea here is that a relationship between two things doesn’t prove that one thing causes the other. When two variables rise and fall together, that’s a correlation, but it doesn’t tell you why they’re linked. There are common reasons the link appears even if there’s no direct cause-and-effect, such as a third factor that influences both, or the possibility that the effect actually influences the supposed cause, or just random coincidence. This is why simply seeing two things move together isn’t enough to claim causation.

Think of a real-world example: ice cream sales and swimming incidents both increase in the summer. It would be incorrect to say ice cream causes drownings. The real driver is the warm weather, which boosts both activities. Establishing causation requires stronger evidence: showing the cause happens before the effect, ruling out other explanations, and ideally using controlled studies or experiments to isolate the effect.

Other terms like misleading data, evidence in general, or authority don’t capture this specific caution. They refer to broader ideas about information quality, support for claims, or relying on experts, but they don’t address the essential distinction that correlation does not equal causation.