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Published online by Cambridge University Press: 25 May 2022
Forster and Sober (1994) introduced the “sub-family problem” for model selection criteria that recommend balancing goodness-of-fit against simplicity. This problem arises when a maximally simple model (family of hypotheses) is artificially constructed to have excellent fit with the data. We argue that the problem arises because of a violation of the general maxim that balancing goodness-of-fit against simplicity leads to desirable inferences only if one is comparing models for the consideration of which one has a positive reason independently of the current data.