Analyzing the Relationship Between Sample Size and Statistical Significance in Hypothesis Testing Frameworks

Posted: Dec 17, 2022

• Charlotte Mitchell
• Charlotte Rivera
• Charlotte Smith

Abstract

This research examines the complex relationship between sample size and statistical significance, challenging traditional power analysis approaches. The study introduces a novel Quantum Statistical Resonance (QSR) framework that conceptualizes statistical significance as an emergent property arising from interactions between sample characteristics and analytical procedures. The research addresses three primary questions: how sample heterogeneity modulates statistical significance attainment, whether optimal sample sizes exist beyond conventional power calculations, and how significance patterns evolve in high-dimensional data spaces. Using significance landscapes and topological data analysis, the study reveals resonance phenomena and significance instability zones, providing new tools for experimental design and challenging fundamental assumptions in statistical methodology.