statistical model selection

Statistical model selection is the process of choosing the best mathematical framework to explain or predict data among several options. It involves evaluating how well each model fits the data while considering complexity—favoring models that are simple yet accurate. Techniques like criteria or tests help compare models to prevent overfitting (too complex) or underfitting (too simple). The goal is to find the most appropriate model that balances accuracy and simplicity, allowing reliable insights and predictions without unnecessary complications.