P>0.05是好的:基于已知的风险,成本和收益的几种假设分析的NORD-h协议
Alessandro Rovetta1, Mohammad Ali Mansournia1,2
1International Committee Against the Misuse of Statistical Significance, Bovezzo, Italy.
概括
医学中的统计测试往往误解了低p值. 这项研究引入了一种新的方法来评估对目标假设的统计性惊喜,提高公共卫生方面的证据可靠性.
科学领域:
- 医学统计 医学统计
- 流行病学 流行病学
- 公共卫生研究 公共卫生研究
背景情况:
- 医学中的统计测试容易产生常见的误解和谬误,如虚数论和二分论.
- 这些误解可能导致临床和流行病学研究中的重大错误,可能会影响患者的安全和健康结果.
- 一个普遍存在的错误是"高显著性谬论",优先考虑低p值,即使在某些假设中高p值是可取的.
研究的目的:
- 为了解决统计解释中高显著性的谬误.
- 提出一种超越零假设测试的新型统计方法.
- 为了正式制定包含利益相关者特定成本,风险和利益的间隔假设.
主要方法:
- 开发一种方法,通过比较实验结果与目标假设的预测来评估统计惊喜.
- 引入NORD-h协议,以使用先前的成本,风险和收益信息来正式确定间隔假设.
- 使用不兼容图 (惊喜图) 来分析统计关系.
主要成果:
- 拟议的方法为传统的零假设显著性测试提供了替代方案.
- 它允许评估相对于预定义的目标假设的统计惊喜.
- 该NORD-h协议提供了一个框架,用于将实际考虑纳入统计推理.
结论:
- 新的统计方法可以减轻医学研究中的解释和认知错误.
- 这种方法有可能提高公共卫生方面的证据的可靠性和有效性.
- 描述性, (几乎) 无条件的统计方法对于得出坚实的结论至关重要,考虑到所有可能性和研究局限性.
相关概念视频
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