The Information Criterion Literature

The Information Criterion Literature refers to a set of statistical tools used to compare and select the best model from multiple options. These criteria evaluate models based on how well they fit the data while penalizing for complexity to prevent overfitting. Common examples include AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion). By balancing goodness-of-fit with simplicity, these criteria help researchers choose models that are both accurate and parsimonious, improving the reliability of predictions and insights drawn from data analysis.