[lellotope]

It's been said before but needs to be said again: this is happening everywhere in the biomedical and related fields. The neurosciences, oncology... the list goes on and on. Anyone take a look at AI research lately? How much tweaking is going on there? How much is your big data finding due to the idiosyncracies of your particular dataset?

Psychology, as has historically always been the case--meta-analysis itself bloomed largely from the field--is the one turning inward and looking at itself. And it's getting crap from people who love to use it as their favorite punching bag.

The irony of this article is that it's psychologists looking at other psychologists, doing the math etc.

The truth is closer to the second hypothesis by the author: bullshit is incentivized everywhere in academics. Reality is less interesting, harder, more incremental. Everyone wants the next genius savior to point to because it's a simpler story than reality. Sexy means more pubs, more grant money.

[m_mueller]

I’ve recently been at a master students’ thesis defense where a method was discussed that is supposed to speed up algorithms by deciding whether to run them on GPU or CPU. The method is to look at the program code with deep learning algorithms.

Fair enough. There’s apparently a set of benchmarks that other papers used and were reused here to compare against. Yet the student was unable to answer my question as to how bias in this dataset was treated or even what percentage of the algorithms was faster on GPU to begin with, they only showed how much better their deep learning fit the data. As in, if 99% is faster on GPU I could as well just provide that as a static answer and be better than any machine learning.

I frankly find it tragic that this kind of stuff is not ironed out in academia. This was was in a very highly ranked university.