Making Effective Statistical Inferences: From Significance Testing to the Open Science Inference Ecosystem (2016-2026)

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.