More broadly, there is a crisis. Our statistical methods/understandings are not working out in these large N-dimensional data sets, at least for the researchers that were raised on excel and not numpy.
Aside: I'm surprised that the FAANGs haven't revolutionized statistics yet. When you have 'phase changes' where a LOT more data becomes available, you get to see very low probability events. It's happened in psych, in bio, in physics most famously, in politics, in economics, etc. We have a LOT more data now in for statistical use, but it's still just Poisson distributions and t-tests. What gives?
But my experience in software companies has been that in a corporation you are a slave to convention. Because even if you innovate in your domain, that innovation will be invisible to the rest of the corporation. Because when they look at it, they will evaluate it by asking which conventions it follows. But inventions by definition don’t follow conventions. What people like is to see conventions followed, and the results of that. It reads as progress.
Now I think it’s primarily artists who invent. But it’s hard to much production as a solo artist. So those who succeed are artists who split their time between doing their work in secret and public ally playing the game of performing conventions in a corporate body. If you split your time that way, you will be praised for applying convention and then periodically your secret art will appear as a magic trick and delight people who are already happy with you.
Without the convention theater, the art will just strike them as confirmation that you’re out touch with the organization.
The same is true in academia really. It’s just a different set of convention theater, around publishing rather than sales.
What does that even mean?
Similarly, with all this 'big data', I would have guessed that we would have come up with something kinda like that. Finally, we have all this stuff coming in. It's all fairly well captured, fairly well correlated, and fairly valuable so you can pay people to just screw around in the labs and see what drops out.
It's not like when we got a lot of experience gathering physical data we suddenly realized we were doing integrals and derivatives wrong.
Math is a brach of logic, sure, but the computer has acted like a microscope for math already. We can more easily check our conjectures and see if they hold true. Also, we've been able to communicate better, so that has helped out math as well. Even though math is 'above' us little humans, the day-to-day work of mathematicians has been helped out a lot, even in the small ways of not having to lug your mortal-coil through the library stacks.
So, with all this big data, I'd have guessed that we could have seen more edge cases with statistics. Little areas where someone said 'huh that's funny'.
Namely that the field seems to be doubling down on increasingly hard computational problems, with deminishing returns, rather than generating wholly new avenues of exploration or insights.
Of course, I've no idea if this is a valid complaint about either neuroscience or theoretical physics.