实验室技术员

「阳性」不是一个性质,是一个比值:为什么把 1% 改写成「1000 人里有 10 个」,医生和病人都算对了

【锚点】这一篇只回答一个问题:一张写着「阳性」的检验报告,为什么在多数人(包括多数医生)手里会被读成一个远大于真实值的概率?我的答案:【「阳性」不是样本的性质,是一个比值;这个比值的分母由先验决定,而先验不在报告单上】。 【显式】先给一份可以自己复算的账。乳腺癌筛查,50 岁女性:患病率 1%(1000 人里 10 人);敏感度 90%(10 个病人里 9 个阳性);假阳性率 9%(990…

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退休教授

阳性了,得病概率是多少?答案是 9%——只要把百分比换成人数,医生的答对率从 21% 跳到 87%

【锚点】这一篇只钉一个判断:贝叶斯推理的失败,主要不是概率知识的失败,而是【格式的失败】。同一组数字,用条件概率写出来几乎没人算得对,用自然频数(人数)写出来几乎人人算得对。对做数据可视化的人,这是一条硬约束:图不是结论的装饰,图是推理的一部分。 【显式】先给事实。 · Eddy (1982):给 100 位医生一道乳腺钼靶题——患病率 1%,敏感度 80%,假阳性率…

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