Statistical methods: Basic concepts, interpretations, and cautions

2025-08-13

AI Summary

Statistical methods face persistent conceptual disagreements, interpretive ambiguities, and practical controversies across disciplines, exacerbated by textbook and journal guidelines that propagate a monolithic paradigm while obscuring foundational uncertainties. Human cognitive limitations and fragmented domain knowledge further impede rigorous statistical reasoning. Method: We propose a critical statistical thinking framework that rejects dogmatic interpretations of *p*-values and confidence intervals. Instead, it advocates descriptive modeling as an initial step, explicit documentation of assumption dependencies, systematic cross-disciplinary literature comparison, and integration of epistemological reflection with empirical constraint assessment. Contribution/Results: This reframes statistical inference as plausible reasoning grounded in inherently unverifiable premises—not definitive conclusions. The framework enhances transparency, reproducibility, and interdisciplinary communicability of statistical practice, fostering a more reflective, evidence-informed, and consensual methodological discourse.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Security and Privacy: Data transparency and provenance
• Responsible Web: Consent frameworks and practices on the web

Abstract

The study of associations and their causal explanations is a central research activity whose methodology varies tremendously across fields. Even within specialized subfields, comparisons across textbooks and journals reveals that the basics are subject to considerable variation and controversy. This variation is often obscured by the singular viewpoints presented within textbooks and journal guidelines, which may be deceptively written as if the norms they adopt are unchallenged. Furthermore, human limitations and the vastness within fields imply that no one can have expertise across all subfields and that interpretations will be severely constrained by the limitations of studies of human populations. The present chapter outlines an approach to statistical methods that attempts to recognize these problems from the start, rather than assume they are absent as in the claims of 'statistical significance' and 'confidence' ordinarily attached to statistical tests and interval estimates. It does so by grounding models and statistics in data description, and treating inferences from them as speculations based on assumptions that cannot be fully validated or checked using the analysis data.

Problem

• Addresses variation in statistical methods across fields
• Challenges deceptive norms in textbooks and guidelines
• Proposes grounded models treating inferences as speculations

Innovation

• Grounds models in data description
• Treats inferences as speculative assumptions
• Rejects traditional statistical significance claims