[mc_maurer]

Ecology PhD turned data scientist, I was looking to respond and you summed up my thoughts really well!

I will add that funding can complicate things a bit, funding sources often get wowed by more "advanced" methods, while the underlying science might be less than stellar. There are important questions that can be answered by small, elegant field studies, and there are questions that require larger datasets and more computation. When we start putting the methodological cart before the scientific horse, that's where we run into problems.

[mc_maurer]

I'd also add that the best scientists I know have, for the duration of their careers, put the question first and pursued methods to fit. I know folks who have the wildest set of skills, from next-gen sequencing to fish tattooing and all sorts of random engineering skills. Willingness to learn new skills in the pursuit of worthwhile questions is one of the hallmarks of a good scientist, in my experience.

[tony_cannistra]

100 percent. I'm guilty of doing this wrong in the beginning of my PhD, and it was the biggest hurdle I needed to learn how to overcome. It's easy to try to force problems onto methodologies; it's much harder (and more interesting!) to try to solve real problems with the best available tool for the job.