Xiao-Li wrote an article on his experiences putting together a statistics course for non-statistics students at Harvard. Xiao-Li asked for any comments, so I’m giving some right here:
I think the ideas in the article are excellent.
The challenges of getting students actively involved in statistics learning have motivated me to write a book on teaching statistics, develop a course on training graduate students to teach statistics, and even to offer general advice on the topic.
But I have not put it all together into a successful introductory course the way Xiao-Li has, and so I read his article with interest, seeking tips in how we can do better in our undergraduate teaching.
The only thing I really disagree with is Xiao-Li’s description of statisticians as “traffic cops on the information highway.” Sure, it sounds good, but often I find my most important role as a statistician is to tell people it’s ok to look at their data, it’s ok to fit their models and graph their inferences. There’s always time to go back and check for statistical significance, but I’ve found the biggest mistakes are when scientists, fearing the statistician over their shoulder, discard much of their information and don’t spend enough time looking at what they have left.
I’m certainly not arguing that simple methods are all we need. (See here for my recent advertisement for fancy modeling). What I’m saying is that I’m happier being an enabler than a police officer. I think I’ve done more good by saying yes than by saying no.
On the other hand, in Xiao-Li’s defense, he’s prevented three false discoveries (see bottom of page 206 of his article), whereas I’ve proved one false theorem. So perhaps we just put different values on our Type 1 and Type 2 errors!
To return to XL’s article, on pages 207-208 he tells a story involving a scientist who was stopped just in time before making a big mistake, by discussing the questionable analysis with Policeman Meng, who noticed the problem. I assume we can all agree that the crucial step in this process was that the scientist was (a) worried that something might be wrong and (b) went to a statistician for help. I’d like to believe that many of the readers of this article would’ve been able to find the problem, but this sort of eagle-eyed criticism is different from what I think of as the most common bit of policing, which is statisticians giving scientists a hard time about technicalities.
Or, to put it another way, I don’t mind the statistician as critic, but I don’t think we should have the police officer’s traditional power to arrest and detain people at will. Except maybe in some extraordinary cases.
To return to undergraduate education: I’ve taught undergraduate statistics several times at Berkeley and at Columbia. Berkeley had an exciting undergraduate program with about 15 juniors and seniors taking a bunch of topics classes. I have fond memories of my survey sampling and decision analysis classes and also of the department’s annual graduation ceremony, which included B.A.’s, M.A.’s, and Ph.D.’s in one big celebration. I’ve heard that the program has since grown to about 50 students. At Columbia, in contrast, we have something in the neighborhood of 0 statistics majors. It’s a feedback loop: few courses, few students, few courses, etc. I think this was the case at Harvard for many many years, although maybe it’s changed recently.
My point? The intro courses at Berkeley for non-majors were very well organized, much more so than at Columbia, at least until recently. Perhaps no coincidence. I suspect it’s easier to confidently teach statistics to non-majors if you have a good relationship with the select group of undergraduates who are interested enough in statistics to major in it. And, conversely, an excellent suite of introductory statistics classes is a great way to interest students in further study.
Teacher training is also important, as Xiao-Li indicates in the last sentence of his article. At Berkeley there was no formal course in statistics teaching, but most of the Ph.D. students went through the “boot camp” of serving as T.A.’s in large courses under the supervision of experienced lecturers such as Roger Purves; between this direct experience and word-of-mouth guidance from other students in the doctoral program, they quickly learned which way was up. At Columbia we have recently revived our course, The Teaching of Statistics at the University Level, and I hope that this course–and similar efforts at Harvard and other universities–will help move us in the right direction.
In addition, wider awareness of statistical issues outside of academia (for example, at our sister blog) will, I hope, make college students demand statistical thinking in all their classes, whether taught by statisticians or not. It wouldn’t be a bad thing for a student in a purely qualitatively-taught history class to consider the role of selection bias in the gathering of historical data (see Part 2 of A Quantitative Tour for more on this sort of thing), just as it isn’t a bad thing for a student in a statistics class to think about the social implications of some of the methods we use.