 One reason for investigating statistical approaches to simplicity is a dissatisfaction with the vagaries of the a priori and naturalistic approaches. Statisticians have come up with a variety of numerically specific proposals for the trade-off between simplicity and goodness of fit. However, these alternative proposals disagree about the ‘cost’ associated with more complex hypotheses. Two leading contenders in the recent literature on model selection are the Akaike Information Criterion [AIC] and the Bayesian Information Criterion [BIC]. AIC directs theorists to choose the model with the highest value of \(\{\log L(\Theta_k )/n\} - k/n,\) where \(\Theta_k\) is the best-fitting member of the class of curves of polynomial degree \(k\), \(\log L\) is log-likelihood, and \(n\) is the sample size. By contrast, BIC maximizes the value of \(\{\log L(\Theta_k )/n\} - k\log[n]/2n.\) In effect, BIC gives an extra positive weighting to simplicity by a factor of \(\log[n]/2\) (where \(n\) is the size of the sample).[16]