None of the Above
In grad school, I had conversations with a couple different conversations with fellow PhD students in the life sciences. The question I asked was not about p-values &#x2F; hypothesis testing but about the (lack of use) of something else technical (I don’t recall the specific question). They all responded, “we haven’t done math since our intro to calculus class and we don’t know how to code.”<p>I feel like that is really at the heart of the problem. It is difficult to create the kind of bespoke scientific model required and use it to perform statistical analysis when you lack the mathematical and computational background. If you don’t know what you’re doing, it’s very easy to misapply existing statistical tests, let alone understand why you might care about something like the bias and variance of your estimator. It’s an understandable problem — if you are a biologist, you want to think about biology, not statistics and most biology work doesn’t require math so it appeals to the scientifically-minded that dislike math and programming.<p>I’m curious if people here who studied those subjects think this intuition tracks, or if I am way off base. If it is part of the problem, I don’t know what the solution is beyond requiring more mathematical and computational literacy, which likely comes at the cost of discipline-specific material.