Abandon Statistical Significance
Abandon Statistical Significance
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DOI：
10.1080/00031305.2018.1527253
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发表时间：
2019-01-01
影响因子：
1.8
通讯作者：
Tackett, Jennifer L.
中科院分区：
文献类型：
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作者：
McShane, Blakeley B.;Gal, David;Tackett, Jennifer L.
我们讨论了零假设显著性检验（NHST）范式在生物医学和社会科学中的复制和更广泛的问题，以及这些问题如何通过涉及修改后的p值阈值，置信区间和贝叶斯因子的建议仍未得到解决。然后，我们讨论我们自己的建议，即放弃统计显著性。我们建议放弃NHST范式及其固有的p值阈值作为生物医学和社会科学研究，出版和发现的默认统计范式。具体而言，我们建议将p值从其阈值筛选角色降级，而是连续处理，与当前从属因素（例如，相关的先前证据、机制的可验证性、研究设计和数据质量、真实的世界成本和收益、发现的新奇以及因研究领域而异的其他因素）仅作为许多证据之一。我们无意“禁止”p值或其他纯粹的统计指标。相反，我们认为，这些措施不应设定门槛，而且，无论是否设定门槛，它们都不应优先于目前的次要因素。我们还认为，将证据校准为p值或其他纯统计指标的函数很少有意义。我们提供了建议，我们的建议如何可以在科学出版过程中，以及在统计决策更广泛地实施。
We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value thresholds, confidence intervals, and Bayes factors. We then discuss our own proposal, which is to abandon statistical significance. We recommend dropping the NHST paradigm-and the p-value thresholds intrinsic to it-as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences. Specifically, we propose that the p-value be demoted from its threshold screening role and instead, treated continuously, be considered along with currently subordinate factors (e.g., related prior evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain) as just one among many pieces of evidence. We have no desire to "ban" p-values or other purely statistical measures. Rather, we believe that such measures should not be thresholded and that, thresholded or not, they should not take priority over the currently subordinate factors. We also argue that it seldom makes sense to calibrate evidence as a function of p-values or other purely statistical measures. We offer recommendations for how our proposal can be implemented in the scientific publication process as well as in statistical decision making more broadly.