🤖 AI Summary
Traditional significance testing often fosters dichotomous thinking, misinterpretation, and irreproducibility, thereby undermining robust scientific inference. This work proposes a unified evidence- and decision-oriented inferential framework that integrates advances in statistical inference and open science practices from 2016 to 2026. Moving beyond sole reliance on p-values, the framework incorporates compatibility interpretations, S-values, smallest effect sizes of interest (SESOI) equivalence testing, Bayesian workflows, and e-value sequential inference. It is further embedded within open science infrastructure—including preregistration, registered reports, multiverse analyses, and adherence to PRISMA 2020 and CONSORT 2025 reporting standards. By synergistically advancing methodological innovation and institutional reform, this open science inference system substantially enhances research transparency, reproducibility, and the quality of scientific decision-making.
📝 Abstract
Statistical inference has undergone a profound transformation over the past decade, evolving from a significance-testing paradigm toward a comprehensive, transparency-driven framework embedded within the broader open science ecosystem. While traditional approaches such as null hypothesis significance testing (NHST) remain widely used, they have been increasingly criticised for fostering dichotomous thinking, misinterpretation, and irreproducible findings. This review synthesises developments from 2016 to 2026, integrating methodological advances-including compatibility-based interpretation of p-values, S-values, equivalence testing with smallest effect sizes of interest (SESOI), Bayesian workflow, and sequential inference using e-values-with systemic reforms such as preregistration, Registered Reports, multiverse analysis, and updated reporting standards (PRISMA 2020, CONSORT 2025). A central contribution of this article is the conceptual unification of statistical inference into two complementary domains: evidence-centric inference, which quantifies compatibility between data and models, and decision-centric inference, which guides actions under uncertainty. By embedding statistical tools within transparent and reproducible research workflows, the modern inferential paradigm moves beyond single-metric evaluation toward a multidimensional assessment of evidence and practical relevance.