Evaluating the Relationship Between Statistical Method Selection and Result Interpretability in Applied Research

Posted: Jul 14, 2016

• Zoey Robinson
• Theodore Baker
• Luna Adams

Abstract

This study investigates the critical relationship between statistical method selection and the interpretability of research findings across applied disciplines. While methodological rigor has traditionally been prioritized in statistical training and application, we demonstrate that interpretability—the ease with which non-specialist stakeholders can understand and apply research findings—represents an equally important dimension of methodological quality. Through a mixed-methods approach combining systematic analysis of 347 published studies from psychology, public health, education, and business research with experimental interventions involving 184 practitioners, we establish that method complexity inversely correlates with interpretability scores across all domains. More significantly, we identify specific methodological characteristics—including effect size reporting practices, visualization techniques, and statistical communication strategies—that mediate this relationship. Our findings challenge the prevailing assumption that methodological sophistication necessarily enhances research utility, revealing instead that inappropriate method selection creates interpretability barriers that undermine practical application. We propose a novel interpretability-weighted methodological framework that balances statistical rigor with communicative clarity, offering researchers practical guidance for method selection that optimizes both technical validity and real-world impact. This research contributes to the growing literature on research translation by providing empirical evidence for interpretability as a measurable dimension of methodological quality and establishing concrete strategies for improving the accessibility of statistical findings without compromising analytical integrity.