Statistical Significance vs. Practical Significance: Analyzing the Roots of the Replicability Crisis in Modern Scientific Research

Abstract

Modern scientific research is facing a severe crisis of reproducibility, with numerous published findings failing to replicate in subsequent independent verification, severely undermining the reliability of scientific knowledge. This paper systematically argues that the methodological imbalance between statistical significance and practical significance constitutes the core root cause of this crisis. We delve into how the misuse of p-values in current research practices—through p-value manipulation and selective reporting—spawns fragile findings. We also reveal the deep-seated impacts of academic incentive structures, publication bias, cognitive biases, and insufficient scientific literacy. Building on this analysis, we propose multidimensional solutions: at the research culture level, reforming incentive mechanisms to prioritize replicable studies and open science practices; and at the educational level, strengthening the cultivation of statistical thinking. The scientific community must achieve a paradigm shift from pursuing statistical significance to evaluating evidence strength and practical relevance, thereby building a more resilient and credible research ecosystem.