AICc (Corrected Akaike Information Criterion)

The AICc (Corrected Akaike Information Criterion) is a statistical tool used to compare different models and identify which best explains data while balancing complexity. It evaluates how well each model fits the data and penalizes overly complex models that might fit noise rather than true patterns. The correction (AICc) adjusts this measure for small sample sizes, providing a more reliable comparison when data are limited. Essentially, it helps researchers choose a model that offers the best balance between accuracy and simplicity, promoting better generalization to new data.