Abstract
Evidence-based research (EBR) underpins scientific progress, clinical practice, and policy development by emphasizing empirical data over intuition. However, systemic vulnerabilities across its lifecycle can compromise validity, reproducibility, and societal impact. This review critically analyzes 28 key pitfalls in EBR, systematically categorized into methodological, statistical, ethical and reporting, human-related, and institutional domains. Drawing on interdisciplinary examples from medicine, psychology, epidemiology, social sciences, and emerging fields like AI-driven research, each pitfall is dissected with precise definitions, real-world case studies, quantitative insights where applicable, and evidence-supported mitigation strategies. Enhanced analysis incorporates recent advancements, such as AI-assisted bias detection and open science platforms, to deepen understanding of inter-pitfall interactions and long-term consequences like the reproducibility crisis.
This review culminates in actionable guidelines for researchers, institutions, and funders to foster resilient EBR ecosystems. By prioritizing rigorous design, transparent processes, and ethical innovation, this framework aims to elevate research quality, minimize errors, and maximize contributions to global knowledge and well-being.
Introduction
Evidence-based research (EBR) represents a paradigm shift in knowledge generation, prioritizing systematic empirical evidence to inform decisions in healthcare, education, environmental policy, and beyond. Since its formalization in the 1990s, EBR has facilitated breakthroughs, such as evidence-informed COVID-19 interventions and climate modeling refinements. Yet, its foundation is precarious: flaws in design, analysis, ethics, human judgment, or systemic structures can propagate misinformation, fuel skepticism, and stall progress. High-profile cases, like the replication failures in psychology (e.g., the 2015 Open Science Collaboration revealing only 36% reproducibility [33]) and retracted medical studies (e.g., over 1,000 COVID-era papers withdrawn by 2023), underscore these risks [30] [31].
We categorize 28 pitfalls into five domains: methodological (flaws in foundational setup), statistical (analytical missteps), ethical and reporting (transparency and moral lapses), human-related (cognitive distortions), and institutional (ecosystemic pressures).
For each, we provide: a definition (D) with theoretical grounding; expanded examples (E) with quantitative impacts where data exists; and multifaceted mitigation strategies (M) emphasizing preventive, detective, and corrective measures. Interconnections are highlighted—e.g., how methodological biases amplify statistical errors—to aid holistic understanding.
The goal is twofold: to offer superior research analysis by cross-referencing pitfalls with emerging literature, and to empower future research through top-tier guidelines. These guidelines synthesize mitigations into prioritized, implementable steps for individuals, teams, and organizations. By addressing these pitfalls proactively, EBR can achieve greater reliability, equity, and innovation, ultimately benefiting society in an era of data abundance and AI augmentation