 Forster and Sober cite Akaike’s (1973) result that an unbiased estimate of the predictive accuracy of a model can be computed by assessing both its goodness of fit and its simplicity as measured by the number of adjustable parameters it contains. A model is a statement (a polynomial, in the case of a proposed curve) that contains at least one adjustable parameter. For any particular model M, a given data set, and identifying \(L(M)\) as the likeliest (i.e. best data fitting) curve from M, Akaike showed that the following expression describes an unbiased estimate of the predictive accuracy of model M: