I’m afraid the job market for stats teachers is not great—most university/college departments I know of don’t hire external instructors to teach stats. Instead they’re often taught by lecturers/asst professors whose research specialties also involve some level of statistical sophistication (that is, “ability to teach stats” as a secondary selection criterion rather than primary). In the US system especially there also tends to be a relatively large pool of advanced PhD students who are much cheaper to employ than tenure track faculty, but who also have the requisite skills to teach stats. Teaching fellows and adjunt faculty (ie people hired just to teach) tend to be hired from within.
Teaching at community college level is a possibility, and strangely enough the pay tends to be much higher than at the university level (shockingly low, if you didn’t already know). But this comes at the cost of a very high workload, and specifically related to your area of interest, you are likely to come across students who lack even the most basic numeracy skills—multiplication and division.
But then it comes to the big question—can one actually teach Bayesian statistics at the undergraduate level? This depends on the field. In psychology, for example, I think the answer at the moment is a definitive “no”. A telling example is John Kruschke at Indiana—one of the main proponents of Bayesian statistics. Here you can see his open letter to the field calling for an end to frequentist approaches. Yet in his introductory undergraduate stats course he explicitly avoids Bayesian statisics (see his course notes here ) and even at the graduate level he sticks mainly to frequentist approaches, albeit with a healthy dose “take the next course which explains how to do things right). Although it’s clear that the transition is coming in this field (for example the message being delivered by EJ Wagenmakers and many others) it has not yet trickled down to undergraduates—who need to know frequentist statistics, if for no other reason than to understand this otherwise mystical jargon that appears in all the research articles they are likely to read in their undergraduate careers.
Sorry, I’ve started to feel like I’m rambling … research methods instruction is one of those things I am passionate about, and your post compelled me to register and post for the first time after years of lurking.
I’m afraid the job market for stats teachers is not great—most university/college departments I know of don’t hire external instructors to teach stats. Instead they’re often taught by lecturers/asst professors whose research specialties also involve some level of statistical sophistication (that is, “ability to teach stats” as a secondary selection criterion rather than primary). In the US system especially there also tends to be a relatively large pool of advanced PhD students who are much cheaper to employ than tenure track faculty, but who also have the requisite skills to teach stats. Teaching fellows and adjunt faculty (ie people hired just to teach) tend to be hired from within.
Teaching at community college level is a possibility, and strangely enough the pay tends to be much higher than at the university level (shockingly low, if you didn’t already know). But this comes at the cost of a very high workload, and specifically related to your area of interest, you are likely to come across students who lack even the most basic numeracy skills—multiplication and division.
But then it comes to the big question—can one actually teach Bayesian statistics at the undergraduate level? This depends on the field. In psychology, for example, I think the answer at the moment is a definitive “no”. A telling example is John Kruschke at Indiana—one of the main proponents of Bayesian statistics. Here you can see his open letter to the field calling for an end to frequentist approaches. Yet in his introductory undergraduate stats course he explicitly avoids Bayesian statisics (see his course notes here ) and even at the graduate level he sticks mainly to frequentist approaches, albeit with a healthy dose “take the next course which explains how to do things right). Although it’s clear that the transition is coming in this field (for example the message being delivered by EJ Wagenmakers and many others) it has not yet trickled down to undergraduates—who need to know frequentist statistics, if for no other reason than to understand this otherwise mystical jargon that appears in all the research articles they are likely to read in their undergraduate careers.
Sorry, I’ve started to feel like I’m rambling … research methods instruction is one of those things I am passionate about, and your post compelled me to register and post for the first time after years of lurking.