There are other ways to do biomedical science than p-hacking at large data sets. Some of the best science I've seen were experiments where the result was guara

There are other ways to do biomedical science than p-hacking at large data sets. Some of the best science I've seen were experiments where the result was guaranteed to be one of a few different possibilities - and therefore any concrete result was interesting. That's mostly attributable to good experimental design. Cell A does X under condition 1. Cell A does Y under condition 2. What is the protein/pathway/atom/molecule responsible for triggering condition Y under condition 2 (or conversely triggering X under condition 1). Pick your level of inquiry, discover the answer, remove the answer to test for necessity, and add the answer back to a null background to test for sufficiency. If all are true, you have no found something useful and true. Statistics was only a tool to help you accomplish the above and actually had little to no bearing on the knowledge being created. Good experimental design could create situations where the culminating experiment either presented an indicator or did not, confirming the questioned hypothesis true or false. Those kinds of factual discoveries were the biomedical science I was taught to probe for, where whatever the answer was it would be significant, interesting, and useful. And there is no way to successfully conduct the above experiments in such a way that statistics even can be of much relevance to the scientific question itself. Either you discover a mechanism or you do not, and logically it can only be one of a very few options.