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.

翻译：过去十年，统计推断历经深刻变革，从显著性检验范式逐步演变为嵌入更广泛开放科学生态系统的、以透明性为核心的综合框架。虽然零假设显著性检验等传统方法仍被广泛使用，但其因助长二分思维、引发误读并导致结果不可复现而日益受到批评。本文综述了2016至2026年间的关键进展，整合了方法论创新（包括基于兼容性的p值解读、S值、最小效应量兴趣值的等价性检验、贝叶斯工作流及基于e值的序列推断）与系统性改革（如预注册、注册报告、多宇宙分析及更新后的报告规范PRISMA 2020与CONSORT 2025）。本文的核心贡献在于将统计推断概念性地统一为两个互补领域：以证据为中心的推断（量化数据与模型间的兼容性）和以决策为中心的推断（在不确定性下指导行动）。通过将统计工具嵌入透明且可重复的研究工作流，现代推断范式已从单一指标评估转向对证据强度与实践相关性的多维考量。