Blog: Hilda Bastian, "5 Tips for Understanding Data in Meta-Analyses", 2017

https://blogs.plos.org/absolutely-maybe/2017/07/03/5-tips-for-understanding-data-in-meta-analyses/

 

"Some studies are such whoppers that they overpower all other studies – no matter how many of them there are. I call them Hulks. Hulks might never be challenged, just because of their sheer size – no one will do another study like it again. Which is great when they provide a definitive answer. But not so great when they might not be representative."

 

I think we must assume that the author is familiar with all uses of the term "whopper".

 

Indeed. And it reminds me of the ironic fact that the bigger the study the more likely systematic bias will generate a statistically significant result.

 

I would think it must be incredibly easy to think you are comparing like with like when you may well not be. Or to convince yourself they are close enough.