Two measures may rise or fall together because they share a common driver — or because of timing and coincidence. Briefings often use causal language even when the evidence only shows a pattern.
Your job is to separate what the data shows from what someone concludes it means.
Push one step further before resourcing a fix or changing a program:
What else changed during the same period?
Is there a plausible mechanism linking action to outcome?
Would we expect the same pattern under different conditions?
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