The Correlation ≠ Causation Fallacy
Assumes that because two things move together, one must cause the other.
- •Definition: Assumes that because two things move together, one must cause the other.
- •Impact: Correlation ≠ Causation distorts reasoning by Correlation alone cannot establish direction or causality. Without ruling out other causes, the inference is premature.
- •Identify: Look for patterns like Notice two trends occur together.
What is the Correlation ≠ Causation fallacy?
When two variables correlate, multiple explanations exist: coincidence, a hidden third factor, or reversed causality. Treating correlation as causation skips testing these alternatives.
People lean on this pattern because Causal stories are compelling and simple, and correlations are easy to find in data-rich settings.
- 1Notice two trends occur together.
- 2Assume one trend causes the other.
- 3Ignore confounding variables or reverse causality.
Why the Correlation ≠ Causation fallacy matters
This fallacy distorts reasoning by Correlation alone cannot establish direction or causality. Without ruling out other causes, the inference is premature.. It often shows up in contexts like Debate, Media, Everyday conversation, where quick takes and ambiguity can hide weak arguments.
Examples of Correlation ≠ Causation in Everyday Life
A city links higher bike-share use to rising rents and claims bike lanes cause gentrification, ignoring broader economic drivers.
Why it is fallacious
Correlation alone cannot establish direction or causality. Without ruling out other causes, the inference is premature.
Why people use it
Causal stories are compelling and simple, and correlations are easy to find in data-rich settings.
Recognition
- Causal language (“causes,” “leads to”) attached to mere co-movement.
- Little discussion of controls, confounders, or alternative explanations.
- Timing or mechanisms are vague or missing.
Response
- Ask for evidence ruling out confounders or reversed causality.
- Request experimental or quasi-experimental support.
- Rephrase the claim as a hypothesis needing testing.
- “Correlation ≠ Causation” style claim: Assumes that because two things move together, one must cause the other.
- Watch for phrasing that skips evidence, e.g. "Assumes that because two things move together, one must cause the other"
- Pattern hint: Notice two trends occur together.
Ask for evidence ruling out confounders or reversed causality.
Correlation ≠ Causation is often mistaken for Hasty Generalisation, but the patterns differ. Compare the steps above to see why this fallacy misleads in its own way.
Close variations that are easy to confuse with Correlation ≠ Causation.
Frequently Asked Questions
Correlation ≠ Causation signals a weak reasoning pattern. Even if the conclusion is true, the path to it is unreliable and should be rebuilt with sound support.
Correlation ≠ Causation follows the pattern listed here, while Hasty Generalisation fails in a different way. Looking at the pattern helps choose the right diagnosis.
You will find it in everyday debates, opinion columns, marketing claims, and quick social posts—anywhere speed or emotion encourages shortcuts.
It can feel persuasive, but it remains logically weak. A careful version should replace the fallacious step with evidence or valid structure.