Akaike Information Criterion

The Akaike Information Criterion (AIC) is a tool statisticians use to compare different models that explain data. It balances how well a model fits the data with how simple or complex the model is, avoiding overfitting. A lower AIC value indicates a better model overall, as it suggests the model explains the data well without unnecessary complexity. Think of it as a way to choose the most efficient explanation that captures the important details without being overly complicated.