I do need to read up on those; Jaynes talks about the implications of Cox's theorem but doesn't go into it directly, so I'm only vaguely familiar. Thank you for the reading suggestions. I did plan to talk about those issues in the introduction of the course. Bolstad has an intro section justifying the Bayesian perspective, as well.
I think I picked that particular set of justifications because educators in general don't care about mathematical proofs, they care about what will be useful for the students to know how to do; in biology, the point of knowing statistics is to be able to read and write scientific papers, and the vast majority of papers are written using frequentist statistics. Proofs will not convince them; the fact that top professors are using Bayesian methods might.
My expectation was that I would replace a mediocre frequentist statistics lecturer with an excellent bayesian statistics lecturer within the same class. The class that I TA is taught by multiple professors, and at least one of them teaches from a Bayesian perspective. Professors have ridiculous academic freedom; one professor covers only basic t-tests, while another professor covers everything from linear regressions to the KS test to chi-squared to two-way ANOVA, and it's still the same course listing. So long as the students aren't complaining about failing, the university does not care. The students can try to sign up for a different professor, and will do so if they hear another prof is easier, but they still have to take the class, so even harder professors still have full sections (especially if their version has a reputation for being very useful/educational).
So, assuming that I would be hired to teach statistics and could choose to teach either frequentist or Bayesian, I see very little point in teaching frequentist. I could also reach vastly more students lecturing than I could via tutoring, probably 80ish vs 10ish.
I think the students that are interested in learning Bayesian stats should have the option available; I think there are probably a fair number of students who are smart, savvy, and motivated enough to sign up for a stronger stats course but aren't quite good enough to teach it to themselves.
I think I would almost rather not teach statistics than teach straight frequentist. I am really sick of teaching kids stuff that I know is suboptimal. I mean, I could do a good job of it, but raising the waterline isn't worth being miserable.
I am considering trying to get a job teaching statistics from a Bayesian perspective at the university or community college level, and I figured I should get some advice, both on whether or not that's a good idea and how to go about it.
Some background on myself: I just got my Masters in computational biology, to go along with a double Bachelors in Computer Science and Cell/Molecular Biology. I was in a PhD program but between enjoying teaching more than research and grad school making me unhappy, I decided to get the Masters instead. I've accumulated a bunch of experience as a teaching assistant (about six semesters) and I'm currently working as a Teaching Specialist (which is a fancy title for a full time TA). I'm now in my fourth semester of TAing biostatistics, which is pretty much just introductory statistics with biology examples. However, it's taught from a frequentist perspective.
I like learning, optimizing, teaching, and doing a good job of things I see people doing badly. I also seem to do dramatically better in highly structured environments. So, I've been thinking about trying to find a lecturer position teaching statistics from a Bayesian perspective. All of the really smart professors I know personally who have an opinion on the topic are Bayesians, Less Wrong as a community prefers Bayesianism, and I prefer it. This seems like a good way to get paid to do something I would enjoy and raise the rationality waterline while I'm at it.
So, the first question is whether this is the most efficient way to get paid to promote rationality. I did send in an application to the Center for Modern Rationality but I haven't heard back, so I'm guessing that isn't an option. Teaching Bayesian statistics seems like the second best bet, but there are probably other options I haven't thought of. I could teach biology or programming classes, but I think those would be less optimal uses of my skills.
Next, is this even a viable option for me, given my qualifications? I haven't taken any education classes to speak of (the class on how to be a TA might count but it was a joke). My job searches suggest that community colleges do hire people with Masters to teach, but universities mostly do not. I don't know what it takes to actually get hired in the current economic climate.
I'm also trying to figure out if this is the best career option given my skillset (or at least estimate the opportunity cost in terms of ease of finding jobs and compensation). I have a number of other potential options available: I could try to find a research position in bioinformatics or computational biology, or look for programming positions. Bioinformatics really makes "analyzing sequence data" and that's something I've barely touched since undergrad; my thesis used existing gene alignments. I could probably brush up and learn the current tools if I wanted, but I have hardly any experience in that area. Computational biology might be a better bet, but it's a ridiculously varied field and so far I have not much enjoyed doing research.
I could probably look for programming jobs, but they would mostly not leverage my biology skills; while I am a very good programmer for a biologist, and a very good biologist for a programmer, I'm not amazing at either. I can actually program: my thesis project involved lots of Ruby scripts to generate and manipulate data prior to statistical analysis, and I've also written things like a flocking implementation and a simple vector graphics drawing program. Everything I've written has been just enough to do what I needed it to do. I did not teach myself to program in general, but I did teach myself Ruby, if that helps estimate my level of programming talent. Yudkowsky did just point out that programming potentially pays REALLY well, possibly better than any of my other career options, but that may be limited to very high talent and/or very experienced programmers.
Assuming it is a good idea for me to try to teach statistics, and assuming I have a reasonable shot at finding such a job, is it realistic to try to teach statistics from a Bayesian perspective to undergrads? Frequentist approaches are still pretty common, so the class would almost certainly have to cover them as well, which means there's a LOT of material to cover. Bayesian methods generally involve some amount of calculus, although I have found an introductory textbook which uses minimal calculus. That might be a bit much to cram into a single semester, especially depending on the quality of the students (physics majors can probably handle a lot more than community college Communications majors).
Speaking of books, what books would be good to teach from, and what books should I read to have enough background? I attempted Jaynes' Probability Theory: The Logic of Science but it was a bit too high level for me to fully understand. I have been working my way through Bolstad's Introduction to Bayesian Statistics which is what I would probably teach the course from. Are there any topics that Less Wrong thinks would be essential to cover in an introductory Bayesian statistics course?
Thanks in advance for all advice and suggestions!