后数据严重性如何将测试结果转化为支持或反对相关推断主张的证据
1Department of Economics, Virginia Tech, Blacksburg, VA 24061, USA.
Entropy (Basel, Switzerland)
|January 26, 2024
概括
不知情的统计建模,而不仅仅是显著性测试,破坏了研究可靠性. 提出的替代方案未能解决这一问题,强调需要数据后严重性评估 (SEV) 来评估证据可信度.
科学领域:
- 统计 统计 统计 统计
- 实证研究方法学 实证研究方法学
- 科学完整性 科学完整性
背景情况:
- 目前关于研究可复制性和意义测试的辩论往往忽视了不良统计实践的影响.
- 统计建模和推理的不知情和公式化应用对公布的经验证据的不可信度有很大影响.
- 问题包括统计错误规范,误解频率分析结果,以及曲线拟合策略.
研究的目的:
- 为了确定比之前讨论的更普遍的不可靠的经验证据的原因.
- 批评对频率测试提出的替代方案,证明它们的不足.
- 引入和倡导数据后严重性 (SEV) 评估作为评估证据的方法.
主要方法:
- 统计建模和推理实践的概念分析.
- 批评常见的频率测试程序和建议的替代方案 (例如,置信区间,效果大小).
- 引入数据后严重性 (SEV) 评估框架.
主要成果:
- 不知情地实施统计建模是不可靠的经验证据的主要驱动因素.
- 对频率测试提出的替代方案并不能解决统计错误规范和误解的基本问题.
- 后数据严重性 (SEV) 评估可以将统计结果与真实证据区分开来,并解决统计与实质意义,大样本问题和可重复性等问题.
结论:
- 解决经验证据的不可靠性需要超越显著性测试的表面改革.
- 欧洲统计价值评估为解释统计结果作为证据提供了一个强大的框架.
- 这种方法澄清了基本的统计问题,并提高了科学发现的可信度.
关键词:
效果大小的影响大小.观察到的信任区间.这就是P-hacking.后数据严重性评估后的数据严重性评估.在数据前与数据后的错误概率.复制复制复制复制复制复制复制统计错误的规范 错误的规范统计与实质意义上的意义.不值得信赖的证据证据.更多相关视频
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