Easiest data mistake PMs make: confusing correlation with causation

“Users who do X are more likely to convert. Let’s push everyone to do X.” Sounds reasonable. But it might be completely wrong.

Maybe your most engaged users naturally do X. Maybe another factor drives both behaviors. Maybe it’s just noise.

Classic example: people buy more ice cream and more air conditioners at the same time. Ice cream isn’t causing AC sales - summer is.

Product teams fall into the same trap:

• “This feature launched and retention improved → the feature worked”
• “Power users do this action → make everyone do it”
• “A competitor succeeded with this feature → we need it too”

Sometimes true. Sometimes not.

Correlation gives you a clue. Experiments help you find the cause.

What’s the worst correlation ≠ causation mistake you’ve seen in product?