I am as anti-pomo/critical theory as it gets, but, sadly, this phenomena is very much present in the hard sciences as well. For example, about 10-15 years ago, the big craze in biology was microarrays, and the early papers made crazy promises about everything that they would deliver. With hindsight, it turned out almost all the early papers were fatally flawed and were drastically statistically underpowered: it turns…
Hmmm, I'm not so sure about this. With things like microarrays and other tech, sure there were promises that were too big. But the underlying idea (gene expression matters, there are lots of genes, we should measure them all) is correct. To wit, RNA-seq has transformed cancer biology. And Nanostring is now offering a microarray-like technology with extremely impressive signal to noise. And we still use lots of tools…
I think in general it's actually quite easy to tell which results are hype and which are not. Do a power analysis and estimate what kind of sample size would be required to measure the effect the researchers estimate - if they are off by an order of magnitude, reject.