I think part of it is also the nature of the research. As an engineer, the use of statistics in papers I’ve written to compare outcomes of the proposed method v

I think part of it is also the nature of the research. As an engineer, the use of statistics in papers I’ve written to compare outcomes of the proposed method vs competitors is rudimentary, but that is for a number of reasons: 1. Normally the proposed method exhibits a number of trade offs with other methods. For example, the proposed method might be slower but accommodates a new class of problem existing methods can’t handle. 2. You can’t easily sample problems for comparison from a real world population. Such a data set doesn’t exist. Therefore, even if I was doing very formal hypothesis testing for a given problem, I could still cheat by adjusting the problem specification until my method is better. 3. Collecting data to a statistically significant level can be incredibly time consuming, and that’s just for a specific problem set up. 4. Theoretical analysis is offered to qualitatively, if not quantitatively compare methods. The experimental data serves more as a sanity check for the theory and demonstration that the proposed method is practical to implement and gives sane results. I’m sure in the harder sciences, this is somewhat similar. It also helps that you have a lot of control over experimental design so that the statistics you need to do can be simple by construction. In biology, you have a million compounding factors and little theory to fall back on, so the statistics will be more complicated.