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Old 09-25-2012
sruthi's Avatar
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Default BIAS doubt!!

hey gys!!
cud sm1 explain to me d difference btw CONFOUNDING BIAS and EFFECT MODIFICATION???
hw can u differentiate dem in a q stem??

Also, is selection and sampling bias the same thing???
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Old 09-25-2012
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Originally Posted by sruthi View Post
hey gys!!
cud sm1 explain to me d difference btw CONFOUNDING BIAS and EFFECT MODIFICATION???
hw can u differentiate dem in a q stem??

Also, is selection and sampling bias the same thing???
i used to have the same doubt.....buut when i watched this got cleared
Effect Modification vs Confounding

there are also many similar threads which discussed abt them

regarding sampling and selection bias......many researchers treat selection bias as subset of sampling bias ....some of them treat as independants....
what's their difference is..??-----sampling bias occurs before preferred subjects are grouped i.e not yet grouped as cases or controls(like subjects not representing the population)...
selection bias occurs when grouping has occured already(but mistake has been done in grouping or futher processing control group from hospital records,some groups loss to follow up..)

hope it understands
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sruthi (09-27-2012)
Old 09-25-2012
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Selection Bias: Fa says "Non-random assignment to study groups", the classic example being the hospital records used to get the sample (called the Berkson bias, which is an important subset of Selection Bias.)

Sampling Bias: Subjects aren't representative of the population. Important examples used in Kaplan for sampling bias are non-respondent bias and volunteer bias. In both these subsets, people who would have responded, or only those who did respond, have created bias in our study. How? Because our study lacks information from responders (in the case of non-respondent bias), or lacks information from those who refused to respond (In the case of respondent bias). Therefore, our results will not be generalizable to the entire population.

Venky has added a very helpful way to differentiate it. Plus, another example that I want to share: I'm a researcher who wants to compare the heights of girls and boys in a classroom. If I use heights of only the tall girls and the tall boys in my study, this would be Sampling bias (The tall girls and boys don't represent the entire class full of other students with different heights.) If, however, this time, I have taken information from all the girls and boys (tall, short, variable everyone) but have now assigned them to groups like hispanic girls vs. asian boys. This is selection bias. (The students have not been assigned randomly into groups but instead on a specific basis, which adds bias to my study.) What I should have done instead to avoid the Sampling Bias? Use heights of boys and girls regardless of whether they are tall or short. How could I've avoided the Selection Bias? By choosing to pick boys and girls randomly, like from the attendance list for instance and chosen roll-numbers etc.

Hope it makes sense, please correct me anyone if you feel so!
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