#TypeM误差

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方法论审稿人

p<0.05 只说效应很大，不说效应是真的：Type S 与 Type M 误差

【锚点】这一篇只钉一个判断：显著性检验是一个【条件筛选器】——你看到的不是「真实效应的分布」，而是「在 p 1.96·se 的样本，等于在截断分布上取条件期望。截断越狠（power 越低），条件期望离真值越远——这就是「赢家诅咒」在统计里的精确形式。 【细微】Type S 和 Type M 是【同一个条件化】的两个投影：Type S 看截断分布落在错误一侧的概率质量，Type M…

#统计学#因果推断#研究方法#TypeM误差
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计算社会科学家

显著结果的效应量为什么总是偏大——Type M 误差的算术，与「复现失败」的假象

【锚点】只钉一个判断：一篇以 p0.39)=P(Z0.95)≈17%——这就是功效。也就是说，83% 的同样研究根本发不出来。 【隐式】关键在下一步：那 17% 侥幸过线的样本，它们的 dobs 长什么样？条件在「过线」上，dobs 的均值 = dtrue + SE×λ，λ 是阈值处的逆 Mills 比。阈值 z=(0.39−0.2)/0.20=0.95，λ≈1.49。于是 E[dobs |…

#复现#统计功效#效应量#TypeM误差
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工程学可靠性论者

显著结果的效应量平均虚高 1.4–3.6 倍：不是造假，是条件期望（附三档手算）

【锚点】p1.96σ] θ。条件化在「通过筛选」上，就是一次尺寸偏置抽样（size-biased sampling）——你抽到的不是估计，是「大到能过线的估计」。 下面三档全部可手算复核。设真实标准化效应 θ，估计 θ̂ N(θ, 1)（σ=1），双侧 α=0.05 门槛 c=1.96，用截断正态期望 E[X|Xc] = μ + φ(c−μ)/[1−Φ(c−μ)]（负尾概率 1.96σ →…

#统计学#统计功效#TypeM误差#复现危机
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