Search results
Results from the WOW.Com Content Network
Contrary to common beliefs, adding covariates to the adjustment set Z can introduce bias. [7] A typical counterexample occurs when Z is a common effect of X and Y , [ 8 ] a case in which Z is not a confounder (i.e., the null set is Back-door admissible) and adjusting for Z would create bias known as " collider bias" or " Berkson's paradox ."
One of the best-known examples of Simpson's paradox comes from a study of gender bias among graduate school admissions to University of California, Berkeley.The admission figures for the fall of 1973 showed that men applying were more likely than women to be admitted, and the difference was so large that it was unlikely to be due to chance.
Bias that is introduced at some stage during experimentation or reporting of research. It is often introduced by, or alleviated by, the experimental design . Pages in category "Experimental bias"
Note that the Wikipedia link to lurking variable redirects to confounding. A difficulty often also arises where the third factor, though fundamentally different from A and B, is so closely related to A and/or B as to be confused with them or very difficult to scientifically disentangle from them (see Example 4).
The stronger the confounding of treatment and covariates, and hence the stronger the bias in the analysis of the naive treatment effect, the better the covariates predict whether a unit is treated or not. By having units with similar propensity scores in both treatment and control, such confounding is reduced.
Controls eliminate alternate explanations of experimental results, especially experimental errors and experimenter bias. Many controls are specific to the type of experiment being performed, as in the molecular markers used in SDS-PAGE experiments, and may simply have the purpose of ensuring that the equipment is working properly.
One probable methodological explanation for the obesity paradox in regards to cardiovascular disease is collider stratification bias, which commonly emerges when one restricts or stratifies on a factor (the "collider") that is caused by both the exposure (or its descendants) of an unmeasured variable and the outcome (or its ancestors / risk ...
Berkson's paradox, also known as Berkson's bias, collider bias, or Berkson's fallacy, is a result in conditional probability and statistics which is often found to be counterintuitive, and hence a veridical paradox. It is a complicating factor arising in statistical tests of proportions.