PERSPECTIVE - Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests
PERSPECTIVE - Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests
复制标题
观点 - 研究人员应该进行深思熟虑的评估，而不是零假设显着性检验
DOI：
10.1287/orsc.1100.0557
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
F. Fidler
中科院分区：
文献类型：
--
作者：
A. Schwab;Eric Abrahamson;W. Starbuck;F. Fidler
零假设显著性检验（nsts）受到了很多批评，特别是在过去的二十年。然而，许多行为和社会科学家没有意识到nsts已经引起了越来越多的批评，因此本文总结了主要的批评。这篇文章还推荐了评估研究结果的其他方法。虽然这些建议并不复杂，但它们确实包含了许多行为和社会科学家认为新颖的思维方式。研究者不应制定NHSTs，而应根据具体情境和具体研究目标进行研究评估，并解释评估指标选择的依据。研究人员应通过报告效应大小来显示研究结果的实质性重要性，并应通过说明置信区间来承认不确定性。通过将数据与朴素假设而不是零假设进行比较，研究人员可以挑战自己，发展更好的理论。简约模型更容易理解，它们的泛化也更可靠。稳健的统计方法能容忍样本假设的偏差。
Null-hypothesis significance tests (NHSTs) have received much criticism, especially during the last two decades. Yet many behavioral and social scientists are unaware that NHSTs have drawn increasing criticism, so this essay summarizes key criticisms. The essay also recommends alternative ways of assessing research findings. Although these recommendations are not complex, they do involve ways of thinking that many behavioral and social scientists find novel. Instead of making NHSTs, researchers should adapt their research assessments to specific contexts and specific research goals, and then explain their rationales for selecting assessment indicators. Researchers should show the substantive importance of findings by reporting effect sizes and should acknowledge uncertainty by stating confidence intervals. By comparing data with naive hypotheses rather than with null hypotheses, researchers can challenge themselves to develop better theories. Parsimonious models are easier to understand, and they generalize more reliably. Robust statistical methods tolerate deviations from assumptions about samples.
显着性检验在流行病学研究中发挥作用：对 A. M. Walker 的反应。
影响因子：
12.7
作者：
Fleiss,JL
通讯作者：
Fleiss,JL