Information Criterion

Information Criterion is a statistical tool used to compare different models and help you choose the best one. It balances two aspects: how well the model fits the data and how simple or complex the model is. A better model should fit the data well while being straightforward, avoiding unnecessary complexity. By calculating a score based on these criteria, researchers can select the model that provides the most useful insights without overfitting the data—which means making it too tailored to specific data points rather than generalizing well. Examples include Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).