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

[Andy]

There’s a deluge of scientific studies of all sorts – thousands every day. There’s often a few studies looking for answers on the same topic, but there can be dozens or even hundreds of them. Meta-analysis is a group of statistical techniques that enable data from more than one study to be combined and analyzed as a new dataset.

Meta-analysis didn’t start to spread until the 1970s. Now there are dozens of publications with meta-analyses every day and it takes less than 5 years for the number published in a year to double.* Meta-analytic methods are still a bit of a mystery to many people, though.

I’ve written a couple of “5 things” posts about meta-analysis, but not enough explaining data basics. So here’s the prequel!

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

 

[Sly Saint]

"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."

 

[chrisb]

"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".

 

[Jonathan Edwards]

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.

 

[Barry]

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.