Part of the answer here comes down to the size of the effect.

If the effect size is large, then you can be pretty sloppy with your statistics - in some ways it doesn’t matter because it’s almost a qualitative/binary difference.

Obviously statistics becomes much more important when the effect size shrinks and you are squinting at some data trying to see if a 20% difference is real or not.

Due to the poor foundations in biosciences (everything from the assays themselves, lack of assay replication, errors in interpretation) engineering/optimizing something can be fiendishly hard.