选 - 黑客:传播噪音作为诱
Federico Echenique1, Kevin He2
1Department of Economics, University of California, Berkeley, CA 94720.
概括
将噪音添加到已发布的数据中可以选出数据挖掘中的错误发现. 这种"传播噪音"对不知情的研究人员的影响更大,而不是那些有先前假设的研究人员,从而提高了研究的可信性.
科学领域:
- 统计方法学的统计方法.
- 研究完整性研究完整性
背景情况:
- 数据挖掘,或p-hacking,通过适应众多的统计模型,产生虚假的发现.
- 不知情的研究人员经常成为p-hacking的牺牲品,而有假设的知情研究人员受到的影响较小.
研究的目的:
- 调查将噪声添加到数据中的有效性,作为针对p-hacking的选机制.
- 分析传播噪音如何影响不同类型的研究人员和研究可信度.
主要方法:
- 理论分析噪音如何影响研究人员,有或没有先前假设.
- 随着观测数量的增加而进行非对称分析.
- 在一个可处理的特殊情况下描述最佳噪声水平.
主要成果:
- 传播噪音通过创建"诱"有效地选出p-hacked的发现.
- 参与数据挖掘的不知情的研究人员比知情的研究人员更容易受到噪音的影响.
- 随着数据大小的增加,噪音可以在异面上实现最佳选.
结论:
- 传播噪音是一种隐私工具,可以通过选p-hackers来重新利用它来提高研究可信度.
- 可以确定最佳噪音水平,平衡选效率和潜在的信息丢失.
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