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I'd identify a larger problem, which is teaching anything as a bunch of recipes. My college offered stats in two tracks: There was a one-semester course for sc

I'd identify a larger problem, which is teaching anything as a bunch of recipes. My college offered stats in two tracks: There was a one-semester course for science majors, which was mostly plugging numbers into formulas. The course was utterly baffling for most of the students who took it. There was also a two-semester course for math majors. It was mainly about proofs, but also had time to go into more depth. You have to know the assumptions underlying a formula, if you're expected to prove it. ;-) But it was only taken by the math majors.

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dan-robertson 3y ago
I’m not sure that teaching mathstats more broadly is the answer to frequentist approaches being used by default, or to the recipe problem. I did the whole mathstats thing as a student and spent a bunch of time doing calculus around things like maximum likelihood estimators or unbiased estimators. I don’t think it’s very useful for the application of statistics in practice. I would recommend Statistical Rethinking by Richard McElreath as a course in Bayesian statistics targeted at scientists who want to extract correct, useful conclusions from their experiments, which doesn’t follow the “recipe book” pattern.
- analog31 3y ago
  I'm not sure of that either. For one thing, the students would be blown away. One thing we didn't have when I took stats was computers. I graduated from high school in 1982, and the colleges in my state were just beginning to get computers. I wonder if stats could be approached differently if it could start with nothing but data -- lots of it -- and graphing tools. This could even happen pre-college.
- lmpdev 3y ago
  I don’t know if it’s the “answer”, but it followed a pretty simple logistic curve for our curriculum at uni First course was almost purely frequentist Second was half and half with several weeks approaching the frequentist bayesian approach from both sides Our third stats course was mostly bayesian though self directed as to which point of view was “most appropriate” for the formulated problem sets. I remember roughly half the cohort used a frequentist perspective on a fairly obvious (but not explicit) bayesian assignment and were severely punished by the course coordinator (fairly for a late year subject tbh)

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dan-robertson 3y ago

I’m not sure that teaching mathstats more broadly is the answer to frequentist approaches being used by default, or to the recipe problem. I did the whole mathstats thing as a student and spent a bunch of time doing calculus around things like maximum likelihood estimators or unbiased estimators. I don’t think it’s very useful for the application of statistics in practice. I would recommend Statistical Rethinking by Richard McElreath as a course in Bayesian statistics targeted at scientists who want to extract correct, useful conclusions from their experiments, which doesn’t follow the “recipe book” pattern.

analog31 3y ago
I'm not sure of that either. For one thing, the students would be blown away. One thing we didn't have when I took stats was computers. I graduated from high school in 1982, and the colleges in my state were just beginning to get computers. I wonder if stats could be approached differently if it could start with nothing but data -- lots of it -- and graphing tools. This could even happen pre-college.
lmpdev 3y ago
I don’t know if it’s the “answer”, but it followed a pretty simple logistic curve for our curriculum at uni First course was almost purely frequentist Second was half and half with several weeks approaching the frequentist bayesian approach from both sides Our third stats course was mostly bayesian though self directed as to which point of view was “most appropriate” for the formulated problem sets. I remember roughly half the cohort used a frequentist perspective on a fairly obvious (but not explicit) bayesian assignment and were severely punished by the course coordinator (fairly for a late year subject tbh)