使用模拟来探索采样分布:对忙和奢的推断的解药
1School of Neuroscience & Psychology, University of Glasgow, United Kingdom Guillaume.Rousselet@glasgow.ac.uk.
eNeuro
|October 23, 2025
概括
模拟通过说明抽样分布,揭示单个实验中的不确定性,并突出p-hacking如何在神经科学和心理学研究中膨胀虚假阳性来澄清频率统计数据.
科学领域:
- 神经科学是一个神经科学.
- 心理学 心理学 心理学
- 统计推理 统计推理
背景情况:
- 在神经科学和心理学中常见的频率主义统计依赖于抽样分布.
- 采样分布在统计推理中往往被理解得很差,并未得到充分利用.
研究的目的:
- 通过模拟来演示可视化采样分布.
- 回答关于实验结果和单个实验结果的解释的实际问题.
主要方法:
- 使用模拟 (先验和后期) 来说明采样分布.
- 专注于图形描述,并提供相关性,比例和响应延迟数据的示例.
主要成果:
- 模拟揭示了实验估计中固有的不确定性.
- 他们强调,单个实验的结果往往应该谨慎解释.
- 展示了任意的p值截止值 (p ≤ 0.05) 如何导致文献中虚假阳性率的膨胀.
结论:
- 模拟可以增强对数据生成过程和可变性的理解.
- 强调需要仔细解释单个实验发现的必要性.
- 警告不要过度依赖统计工具,导致广泛的假阳性结果.
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