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Thanks! Yeah I should clarify that these friends I were talking to do the kind of bio / neuroscience where they spend most of their time in a lab running a phys

Thanks! Yeah I should clarify that these friends I were talking to do the kind of bio / neuroscience where they spend most of their time in a lab running a physical experiment so their skills are focused around that kind of wet work and they interact very little with mathematical folks based on what they implied. Also, yes, in any field you have people that miss the science for the method. Sometimes that makes sense if their research is on the method and not the application, but the best papers are strong in both. On point 1, I feel like any kind of statistical cookbook that says “if x do y” will lead to the same problem. Any such recipe will not be able to capture the nuances of problems many scientists face. I’m not sure what the solution is other than some vague “we need better modeling tools” statement that is as wishful as asking people to learn stats better. On point 2, I also agree that’s an issue. In my field, the “future directions,” section is the first thing you cut down to make a page limit, but it’s really an important section to both discuss what the issues with your idea are and how they can be fixed in the future.

Fomite 3y ago
One thing we're trying in my program right now is to introduce "Statistical Thinking" as a mandatory class for both wet lab and computational students, to cover the why of statistics and try to teach people how to think about what they're doing, before they hit the "When X, do Y to collect p-value." stage.
- tnecniv 3y ago
  Another thought I had is to replace statistics requirements with a rigorous (but not measure-theoretic) probability class or, preferably a semester of both. My engineering program required probability, not statistics, so I never took a formal stats class. However, my probability class was excellent. If I need to figure out a statistical method now, I might not know what method or <letter>-test to use, but I google a description of the problem and am greeted with stats websites and papers that solve various cases of the problem that are totally understandable using undergrad probability. The intuition for doing so is that, as this article argues, the hard part is in determining the right probability model describing your experiment. Once you have a good model, running the statistical test is easy (whether there’s a tractable algorithm is another question, but if there’s no tractable way to estimate your parameters, I’d argue as a practitioner a model being too difficult computationally is a good heuristic for your experiment not being robust)

Fomite 3y ago

One thing we're trying in my program right now is to introduce "Statistical Thinking" as a mandatory class for both wet lab and computational students, to cover the why of statistics and try to teach people how to think about what they're doing, before they hit the "When X, do Y to collect p-value." stage.

tnecniv 3y ago
Another thought I had is to replace statistics requirements with a rigorous (but not measure-theoretic) probability class or, preferably a semester of both. My engineering program required probability, not statistics, so I never took a formal stats class. However, my probability class was excellent. If I need to figure out a statistical method now, I might not know what method or <letter>-test to use, but I google a description of the problem and am greeted with stats websites and papers that solve various cases of the problem that are totally understandable using undergrad probability. The intuition for doing so is that, as this article argues, the hard part is in determining the right probability model describing your experiment. Once you have a good model, running the statistical test is easy (whether there’s a tractable algorithm is another question, but if there’s no tractable way to estimate your parameters, I’d argue as a practitioner a model being too difficult computationally is a good heuristic for your experiment not being robust)