"Correlation Isn't Causation," But Do You Know Why?

While most people are aware that correlation doesn't imply causation, few understand why or how to apply this knowledge practically. Correlations come in five distinct types, each requiring a different interpretation and response.

• Only one type represents direct causation (A causes B).
• Spurious coincidences
• Reverse causation (B causes A)
• Confounding variables (C causes both A and B)
• Feedback loops (A and B mutually reinforce each other)

When faced with a deluge of information, many a marketer will fall prey to the data dredging trap, leading to drawing false conclusions from the random patterns that appear. Those patterns can appear remarkably convincing, especially when analyzing multiple variables simultaneously. This "data dredging" effect explains why so many research findings fail to replicate and why misleading correlations flood social media and information outlets.

Try this instead. Or don't.

Before concluding that A causes B, systematically ask: Is this a pure coincidence? Could B cause A? Might both stem from an unseen factor C? Are A and B reinforcing each other in a feedback loop? This methodical approach transforms correlation analysis from a reasoning trap into a competitive advantage. By correctly identifying the type of correlation you're observing, you can avoid costly misinterpretations, spot genuine causal relationships others miss, and make decisions based on how variables influence each other rather than how they merely appear to connect.

Some correlations are real but misleading. Others are simply due to chance. Understanding which is which can help you avoid common reasoning errors and make better decisions.