The Impact of Large Sample Approximations on Statistical Efficiency and Model Simplification Strategies

Posted: Mar 08, 2007

• David Torres
• Elijah Hill
• Elijah Lopez

Abstract

The reliance on large sample approximations represents a fundamental pillar of modern statistical practice, underpinning everything from hypothesis testing and confidence interval construction to model selection and simplification strategies. These approximations, rooted in asymptotic theory, provide elegant mathematical frameworks that enable tractable inference in complex statistical models. However, the practical application of these theoretical constructs often occurs in finite-sample regimes where the underlying assumptions may not hold, potentially leading to significant efficiency losses and biased inference. This research addresses the critical gap between asymptotic theory and finite-sample practice by systematically investigating how large sample approximations impact statistical efficiency and how this understanding can inform more effective model simplification strategies.