In other words, AIC can be used to form a foundation of statistics that is distinct from both frequentism and Bayesianism. When the sample size is small, there is a substantial probability that AIC will select models that have too many parameters, i.e. that AIC will overfit. To address such potential overfitting, AICc was developed: AICc is AIC with a correction for small sample sizes. The formula for AICc depends upon the statistical model. Assuming that the model is univariate, is linear in its parameters, and has normally-distributed residuals (conditional upon regressors), then the formula for AICc is as follows.  —where denotes the sample size and denotes the number of parameters. Thus, AICc is essentially AIC with an extra penalty term for the number of parameters. Note that as , the extra penalty term converges to 0, and thus AICc converges to AIC. If the assumption that the model is univariate and linear with normal residuals does not hold, then the formula for AICc will generally be different from the formula above. For some models, the formula can be difficult to determine. For every model that has AICc available, though, the formula for AICc is given by AIC plus terms that includes both and 2. In comparison, the formula for AIC includes but not 2. In other words, AIC is a first-order estimate (of the information loss), whereas AICc is a second-order estimate. Further discussion of the formula, with examples of other assumptions, is given by and by . In particular, with other assumptions, bootstrap estimation of the formula is often feasible. To summarize, AICc has the advantage of tending to be more accurate than AIC (especially for small samples), but AICc also has the disadvantage of sometimes being much more difficult to compute than AIC. Note that if all the candidate models have the same and the same formula for AICc, then AICc and AIC will give identical (relative) valuations; hence, there will be no disadvantage in using AIC, instead of AICc. Furthermore, if is many times larger than 2, then the extra penalty term will be negligible; hence, the disadvantage in using AIC, instead of AICc, will be negligible. The Akaike information criterion was formulated by the statistician Hirotugu Akaike. It was originally named "an information criterion".