arXiv: cs.AI preprintsInternational6 November 2026
Adaptive Experimentation Can Hide Model Error:Detection Limits and Falsification Budget
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arXiv:2611.07529v1 Announce Type: cross Abstract: An experiment can be informative about the parameters of a working model and uninformative about whether that model is wrong. Adaptive design can reinforce this mismatch by repeatedly selecting settings where an incorrect model fits. We bound the power of any valid adequacy test in terms of the experimenter's own sampling distribution, then distinguish information absent from the selected data from information lost by the detector. A computable diagnostic measures unresolved error relative to a declared expansion of the working model. It motiva
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