This is one I’d never thought about . . . Daniel Rubenson writes:
I’m an assistant professor in the Politics Department at Ryerson University in Toronto. I will be teaching an intro statistics course soon and I wanted to ask your advice about it. The course is taught to fire fighters in Ontario as part of a certificate program in public administration that they can take. The group is relatively small (15-20 students) and the course is delivered over an intensive 5 day period. It is not entirely clear yet whether we will have access to computers with any statistical software; the course is taught off campus at a training facility run by the Ontario Fire College.
The students will be highly motivated but they will have no statistics training and whatever math training they have will be from high school, probably more than ten years ago. The main purpose of the course will be to train them in understanding and interpreting the results of quantitative research and reports, not so much in actually performing statistical analysis on their own.
Do you have any advice in terms of topics to cover or avoid, particular approaches to take? As I said, the approach you take in the Teaching Statistics book is excellent and I use many of the demonstrations to great effect in my regular statistics classes. In this case both the students and the compressed nature of the course create some unique challenges.
I have no idea what to say on that, except to retreat to my general thoughts about statistics as comparisons. Start with comparing two averages (or the corresponding graph), then adjust for various potential confounders, consider data collection issues, move to regression and Anova. In 5 days, you can do a couple of examples of this sort, then step back and do a little bit of probability and statistical inference.