Model fit refers to how well a statistical model represents the data it is intended to explain. A good model fit indicates that the model accurately captures the underlying patterns in the data, while a poor fit suggests that the model may be missing important elements or is overly complex. The concept of model fit is closely linked to information criteria, which help in selecting the most appropriate model based on its ability to explain the data while penalizing for unnecessary complexity.
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