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

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.