Scientific method


Predicted fit to future data



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Predicted fit to future data
Given plausible assumptions about errors in data, a model that fits a given body of data too closely is likely to be tracking random errors in the data in addition to the lawlike phenomenon under investigation. Statisticians refer to this as “overfitting the data”. They have designed many criteria to reveal cases where a simpler model has better expected fit to future data generated by repetitions of an experiment than a more complex model that better fits the data so far. Among philosophers of science, Malcolm Forster and Elliott Sober have appealed to the Akaike Information Criterion to challenge the assumption that fit to past data exhausts the criteria for scientific inference. This criterion is not sufficient to recover Newton’s method. The extent to which other such proposals can recover Newton’s method is an open question.


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