关于使用接收机运行特征曲线分析来确定最合适的p值显著性值
1Global Virus Network, Middle East Region of Global Virus Network (GVN), Shiraz, Iran. Farrokh.Habibzadeh@gmail.com.
Journal of translational medicine
|January 4, 2024
概括
传统的0.05的p值值没有科学支持. 本研究提出了一种使用接收器运行特征 (ROC) 分析的新方法,以确定最合适的p值值值值值以获得统计学意义,从而最大限度地减少错误.
科学领域:
- 统计方法学的统计方法.
- 生物统计学 生物统计学
- 科学研究的完整性 科学研究的完整性
背景情况:
- 传统的0.05的p值值缺乏强有力的科学依据.
- 以前试图建立替代的固定值 (例如,0.005) 的尝试并没有得到广泛的接受.
- 0.05值的任意性质使准确的统计推断变得复杂.
研究的目的:
- 提出一种数据驱动的方法来确定最佳的p值显著性值.
- 挑战在科学研究中普遍应用0.05值.
- 提高统计结果的可靠性和可解释性.
主要方法:
- 使用接收器操作特征 (ROC) 曲线分析.
- 在诊断测试和统计假设测试之间进行类比.
- 计算最小化I型和II型错误加权总和的值.
主要成果:
- 最佳的p值值不是固定的,并且在研究和统计测试之间有很大的差异.
- 拟议的方法通常建议的值远低于传统的0.05.
- 该方法为统计学意义提供了一个研究特定的,优化的值.
结论:
- 目前使用的p值具有固定的值,对于强大的科学研究是有问题的.
- 频率统计在正确应用不同值方面存在挑战.
- 其他统计方法,如贝叶斯方法,可能为数据分析提供更合适的解决方案.
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