The "most epidemiology studies are wrong", perhaps best advanced by John P. A. Ioannidis in Why Most Published Research Findings Are False both extremely common and somewhat flawed, though there are absolutely some worthwhile points in his article.
Perhaps the one that amuses me the most is how readily it's been embraced conceptually, even though its argument is that...perhaps it shouldn't be.
Goodman and Greenland outline some problems with the paper here: http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0040168
Mainly, the flaw is in how Ioannidis structures his argument, such that the odds are stacked against certain types of studies, and the evidentiary value of effect measures and actual p-values, rather than just a binary "Is/is not >0.05", are discounted.
There are other examples of supposedly "wrong" findings later disproved (this usually goes in the form of an observational epidemiology study finding one thing, and a subsequent RCT finding something different) that can be traced back to the studies asking different things. An examplar paper might be: Hernán MA, Alonso A, Logan R, Grodstein F, Michels KB, Willett WC, Manson JE, Robins JM. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease (with discussion). Epidemiology 2008; 19:766-779.
What I take from all this is the following:
- Significance testing in and of itself is not proof. There's far more value in providing the p-value, and even more value in providing an actual estimate of effect.
- No study can provide singular, definitive proof of something. Not an observational study, and not an RCT (which are far from free of bias). Nor should any study claim to.
- The question of "wrong" is an odd one in science. A more useful question would be "are most published results likely to advance their field?". Because a wrong study that provides fodder for further analysis, new methods and more thoughtful science is still profoundly useful.
There is an additional way to directly estimate the scale of the problem: try to reproduce reported research results. Though there are more reasons than the nature of statistical fluctuations to explain failure to reproduce results, the answers give an order of magnitude for the reliability of published research and Ioannidis' estimate. The botom line of such studies is that something like 2 out of 3 attempts to reproduce previous research fail to do so. For a more detailed summary see the answers here: Can up to 70% of scientific studies not be reproduced?