My experience in research is that there is very little pre-commitment on this level of granularity. Grant applications should be at the level of "We hope to study X because it is known to be correlated with Y and are hoping to elucidate a mechanistic relationship between these two". The experiments which go into proving such should more closely resemble mathematical proofs, is a viewpoint I currently hold.
One of the areas I helped write a paper for as an undergraduate was later proven to have a completely different mechanistic understanding than we proposed. I think having this more detailed set of observations would have helped both us and any people citing our paper come to this conclusion earlier. Physics is great at this [1] - seeing "dark matter" or something unexplainable is an opportunity, but many fields treat presenting something as 'unexplainable' as a weakness.
[0] I don't have experience in particle physics or anything of that nature where you're working on constructing something like the LHC
[1] Once again, outside observer bias
I think I’ll end up in a similar place - hyper-focusing as a graduate student and then transitioning out of academia after. One understated part of academia is that for a lot of fields (Such as the bio-adjacent fields I’m interested in), academia is one of the few places willing to take on trainee risk. I hope we can keep academia as a place where we’re willing to take risks to educate, which I think starts with reforming the processes informed by the critiques this post levies.
One thing I would advocate for is forcing pre-registration of experiments. Publishing a design for experiments with an intent, a hypothesis, and a afterwards a quick summary of the results. That process for FDA trials has naturally led to a large buildup of both positive and negative results, while also inhibiting post-hoc statistical baboonery.