Model adequacy

Model adequacy refers to how well a statistical or predictive model captures the true pattern or relationships in the data it is designed to analyze. It involves checking whether the model's assumptions are valid and if it provides accurate, reliable results. Essentially, a model is adequate if it sufficiently explains or predicts the data without missing important details or exaggerating noise. Ensuring adequacy helps users trust the model’s conclusions and make confident decisions based on its outputs.