 Extreme answers to the trade-off problem seem to be obviously inadequate. Always picking the model with the best fit to the data, regardless of its complexity, faces the prospect (mentioned earlier) of ‘overfitting’ error and noise in the data. Always picking the simplest model, regardless of its fit to the data, cuts the model free from any link to observation or experiment. Forster associates the ‘Always Complex’ and the ‘Always Simple’ rule with empiricism and rationalism  respectively (ibid.).  All the candidate rules that are seriously discussed by statisticians fall in between these two extremes. Yet they differ in their answers over how much weight to give simplicity in its trade-off against goodness of fit. In addition to AIC and BIC, other rules include Neyman-Pearson hypothesis testing, and the minimum description length (MDL) criterion.