Editing Statistical inference (section)

===AIC-based inference===
{{Main|Akaike information criterion}}
{{expand section|date=November 2017}}
The ''[[Akaike information criterion]]'' (AIC) is an [[estimator]] of the relative quality of [[statistical model]]s for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for [[model selection]].

AIC is founded on [[information theory]]: it offers an estimate of the relative information lost when a given model is used to represent the process that generated the data. (In doing so, it deals with the trade-off between the [[goodness of fit]] of the model and the simplicity of the model.)