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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 ."
Confounding is a critical issue in observational studies because it can lead to biased or misleading conclusions about relationships between variables. A confounder is an extraneous variable that is related to both the independent variable (treatment or exposure) and the dependent variable (outcome), potentially distorting the true association.
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.
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"
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.
Selection bias refers to the problem that, at pre-test, differences between groups exist that may interact with the independent variable and thus be 'responsible' for the observed outcome. Researchers and participants bring to the experiment a myriad of characteristics, some learned and others inherent.
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.
Confounding – Variable or factor in causal inference; Confusion of the inverse – Logical fallacy; Curse of the rainbow jersey - example of such a correlation fallacy in sport; French paradox – Observation that heart disease in French people is much less than is expected; Design of experiments – Design of tasks