什么是Regression dilution bias， attenuation bias （衰减偏误）

六出

参考1：
可以翻译成 因果稀释偏差 因果削弱偏差
The gist [of regression dilution bias] is that if an association between two variables--such as salt and blood pressure--is real, any errors in measuring exposure to either variable will only serve to "dilute" the apparent cause and effect
如果两个变量之间的联系-如盐和血压确实存在，对任何1变量的错误测量都会减弱的其因果联系的外在表现
这个词一般多用在医学上   尤其是心血管的 比如血压的测量 常用的还有白大衣效应 white coat effect  观察者偏差observer bias

参考2：
Regression dilution is a statistical phenomenon also known as "attenuation".

Consider fitting a straight line for the relationship of an outcome variable y to a predictor variable x, and estimating the gradient (slope) of the line. Statistical variability, measurement error or random noise in the y variable cause imprecision in the estimated gradient, but not bias: on average, the procedure calculates the right gradient. However, variability, measurement error or random noise in the x variable causes bias in the estimated gradient (as well as imprecision). In a nutshell: the more variability is in the x measurement, the closer the estimated gradient gets to 0 instead of the true gradient. This 'dilution' of the gradient towards 0 is referred to as "regression dilution," "attenuation," or "attenuation bias."