Comment by DoreenMichele
3 years ago
That sounds like you are talking about academia, not science.
Mendel, a man remembered as the father of genetics, was a friar who failed to pass his exams for becoming a certified teacher, so he became an abbot. At that point, his scientific work languished because his duties were too burdensome to allow time for it.
I don't think I am focusing on academia, really.
I've made two major points, first about evidence or the invention of it, the second about models. I also inserted a smaller point on the usage of 'p'.
Evidence does not need a seal of approval from an institution to be considered 'good', just that statistically, using whatever tools we have, evidence has some semblance of validity that could represent something, potentially reality.
Models are those tools in pretty much all of science. Models are all broken, some fundamentally, others in just the nature of cost to develop one that can represent reality. But that's the crux, representing reality must be done cheaply and efficiently as you can, because accurately representing reality has way too much cost in just discovering all the variables to tweak. There will be no time for experiments if that is all you do. So you only have approximations to work with. Basically, you're never actually dealing with reality.
My smaller point of 'p' is just about how we firmly treat confidence intervals as measures of 'solid' research, which I guess can be extrapolated to academia, but I certainly don't think it's limited to academia.
Mendel wasn't trying to model reality. He worked with actual plants.
He's likely not the only scientist concerned with actual reality.
Hopefully I'm not interpreting, but I think latency's point is more epistemological than a comment on "how science is being done". For example, scientists will still use Newtonian mechanics (as opposed to relativistic mechanics) to model reality even though we know it's "wrong". Part of this is pragmatic (tensor arithmetic is computationally expensive), but part of it is also because there's a beauty in the model itself and how it approximates what we observe. Taken to a greater extreme, we can also make Newtonian-like models that don't model observed reality at all, but
Ultimately though, I would say you're right overall - most scientists (in my experience) do want to understand the world/reality, and want to do it accurately.