Epistemological scope and limitations of contemporary statistical inference: a systematic review
DOI:
https://doi.org/10.36097/rsan.v1iEspecial_3.4198Keywords:
Statistical inference, epistemology, statistical significance, causal inferenceAbstract
Statistical inference is fundamental to producing scientific knowledge, interpreting evidence, and guiding decision-making under uncertainty. However, its validity depends on study design, underlying assumptions, analytical transparency, and the appropriate interpretation of results. The objective of this study was to qualitatively synthesize the epistemological scope and limitations attributed to statistical inference in recent scientific literature. A qualitative systematic review guided by PRISMA 2020 was conducted. The PubMed and Europe PMC databases were searched to identify publications issued between 2021 and 2025. Thirteen studies were included and analyzed through a thematic, comparative, and interpretive synthesis. The results were organized into five themes: reinterpretation of the p-value and intervals as measures of compatibility; coexistence of frequentist and Bayesian approaches; reproducibility and quality of statistical reporting; uncertainty quantification in artificial intelligence; and the relationship between causal inference and machine learning. The reviewed literature questions dichotomous decisions based exclusively on statistical significance and emphasizes the need to consider effect sizes, assumptions, context, and practical relevance. It also indicates that inferential validity is shaped by institutional practices, transparency, and study design quality. The study concludes that statistical inference remains essential to scientific reasoning but does not provide definitive certainty. Its rigorous use requires making assumptions explicit, calibrating uncertainty, ensuring analytical transparency, and clearly distinguishing among association, prediction, and causality.
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Copyright (c) 2026 Lissette Beatriz Villavicencio Cedeño, Yoiler Batista Garcet, Sandra Elizabeth Tenelanda Cudco , Deysi Margoth Guanga Chunata , Darwin Casaliglla Ger
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