|Abstract||Puzzle solving in normal science involves a process of accommodation—auxiliary assumptions are changed, and parameter values are adjusted so as to eliminate the known discrepancies with the data. Accommodation is often contrasted with prediction. Predictions happen when one achieves a good fit with novel data without accommodation. So, what exactly is the distinction, and why is it important? The distinction, as I understand it, is relative to a model M and a data set D, where M is a set of equations with adjustable parameters (i. e., M is a family of equations with no free parameters). Definition: Model M predicts data D if and only if either (a) all members of M fit D well, or (b) a particular predictive hypothesis is selected from M by fitting M to other data, and the fitted model fits D well. M merely accommodates D if and only if (i) M does not predict D, and (ii) the predictive hypothesis selected from M using other data does not fit D well. There will be cases in which a model M neither predicts nor accommodates D. These are the cases in which we are willing to say that data falsifies the model. So, the distinction between prediction and accommodation applies only when there is no falsification.|
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