| > How can you tell if you missed one? Similar studies, using as many variables as you can find. Residual confounding is always and ever a problem, but the odds that something is both a strong residual confounder and has never been observed to have an association with the outcome or the exposure is pretty rare? > Why? Without any support, this seems to be an appeal to probability. It's really not - if for no other reason than it's forced you to think about your system more than a simple guess would. It's not an appeal to probability, its using data to update whatever prior you came in with. Guesswork is just using your prior. > The fallacy of moving the goalposts; also the no true Scotsman fallacy. Not really, no. Some observational studies are crap - this is just true. But that doesn't say anything about the potential quality of observational evidence, and many of the commonly raised objections to observational studies are actually objections to poorly run studies. The example used was a study that examined no potential confounding variables, looked at a correlation with no prior evidence suggesting any linkage between the two or biological plausibility, and then asserts that they've found a causal link. That's a bad study. It's not 'No True Scotsman Fallacy' to say that the problems with a bad study don't generalize to all studies. If it is, then we're all screwed, because you can run a bad RCT too. |
Appeal to probability; appeal to common sense.
> Some observational studies are crap - this is just true.
Special pleading.
> But that doesn't say anything about the potential quality of observational evidence
Straw man.
> many of the commonly raised objections to observational studies are actually objections to poorly run studies
> That's a bad study.
Moving the goalposts. No true Scotsman.
> It's not 'No True Scotsman Fallacy' to say that the problems with a bad study don't generalize to all studies.
Straw man.