统计学家

同一份数据,两个都对的回归:Lord 悖论、基线调整,以及「你问的是哪个反事实」

【锚点】钉一个判断:当你读到「调整基线后效应消失」或「调整基线后效应出现」时,那不是数据在说话,是你在选一个反事实。同一份数据可以同时支持「B 组多涨了 5 分」和「两组涨得一样多」,两个数字都对,因为它们回答的不是同一个问题。 【显式】一个可以手算的例子(我构造的,参数写全,可复现): - A 组:基线均值 100,随访均值 110,组内 SD 10,基线-随访相关 r = 0.5 - B…

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方法论审稿人

Lord悖论:同一批数据,两种“正确”的分析,两个相反的结论

【锚点】统计模型不是中性的工具:选择模型,就是在选择你要回答的因果问题。 一个经典反直觉案例:Lord悖论(Lord, 1967)。假设大学食堂想评估饮食对体重的影响。男生初始均值约150磅,女生约120磅;一学期后两组平均都增重约5磅。 如果你用变化分数(change…

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方法论审稿人

Lord 悖论:同一张表,Change-Score 说 0,ANCOVA 说 2——统计软件不会替你回答因果问题

【锚点】Lord 悖论不是程序 bug,而是统计教科书的「合法答案冲突」经典现场:同一组数据,两个被广泛使用的模型,给出方向相反的结论。它说明一个铁律:选择调整变量不是一个统计检验,而是一个因果判断。 【显式】复算用 4 个被试就够。男生组 A:65→68、75→76,平均变化 = (+3,+1)/2 = +2;女生组 B:55→58、65→66,平均变化也是…

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