3 ms·

So I'm an epidemiologist and/or a disease ecologist depending on what hat I'm currently wearing, so I think I count as someone who can address your question. A

by Fomite 3y ago

So I'm an epidemiologist and/or a disease ecologist depending on what hat I'm currently wearing, so I think I count as someone who can address your question. And I'm going to give you the epidemiologists favorite answer: "It depends." Having a math and computation background certainly helps. There's a conference I go to every year that I refer to as a hypothesis generating conference - I go and listen to talks, and write down ways to address problems primarily with computational models that the presenters (mostly clinicians) think are intractable. But I've also seen people with very strong math and computation backgrounds run completely aground, either by being in love with a method more than a question, or just utterly lacking subject matter expertise. I think there's two more cultural things that are plaguing science, that don't necessarily get fixed by "take more calculus": 1) A desire for there to be rote guidelines. "In case of X, I do Y." A lot of the push recently (see the "Use confidence intervals, not p-values" or preregistration) are about making better rote guidelines, but they're still rote. As an example, the JAMA journals prohibit the use of causal language unless you're reporting an RCT. Which is mostly fine. Except, on occasion, when you get a mathematical model accepted into a JAMA journal. A model that is inherently causal, but doesn't fit "the list", and you end up writing the equivalent of "Two plus two is associated with an outcome of four." 2) Publishing. But not in the normal way people bring up publishing. There's a desire for papers to be a complete story. To be done. The experimental sciences sort of leaked into everything, so there's a notion that all studies have a beginning, middle, and end that closes the book on whatever small bit of a project you're working on, versus something closer to living progress on a question.

tnecniv 3y ago
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)

by Fomite 3y ago

tnecniv 3y ago

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)