Ask us anything: the “perfect” intro biostats course

Every year we invite readers to ask us anything! Here’s today’s question, from Pavel Dodonov (paraphrased; click that last link for the original):

How would you structure the “perfect” statistics course for biology undergraduate students?

Jeremy’s answer:

I teach intro biostats, so I wish I could answer by just linking to the course syllabus. 🙂 And I actually do think the course I teach (in alternate terms with my amazing colleague Kyla Flanagan) is a good version of a traditional intro biostats course. But there are some tweaks I’d like to make. More broadly, one can make the case that the traditional intro biostats course has had its day and should be replaced by something very different. So my short answer is that I don’t think there’s any one “perfect” intro biostats course.

Longer answer: here are some things I think our course gets right:

• We teach R in the labs. R coding basics are a highly-transferable skill students can build on in future, no matter what direction life takes them. R is widely used in the private sector, for instance. Every year we get several students taking the course primarily because they want to learn R, often because R wasn’t taught in the stats courses in their home departments.
• We teach permutation tests and bootstrapping instead of classical nonparametric tests. Permutation tests and bootstrapping are clever, elegant ideas that students can grasp and that many get a kick of out of. I feel pretty strongly that there’s no longer any reason for anybody to use or teach Mann-Whitney U tests or Kruskal-Wallis tests or etc. But perhaps I’m overgeneralizing from my own experience! Feel free to disagree in the comments!
• We do a good job teaching the students to think, not just blindly follow set procedures. I really want them to understand why they’re doing the stats they’re doing, and appreciate that all the various statistical tests we teach are just variations on the same underlying logic.
• I think we do a good job engaging the students in statistical material and showing them how being able to think statistically is helpful for any informed citizen. Statistical vignettes are one way among others that I do that. But it’s hard because relatively few students come in to the course with a pre-existing interest in the material. Many students are only there because they’re required to be there.

As to what I’d change, the intro biostats course here at Calgary emphasizes study design and null hypothesis testing. That’s because you’ve got to start somewhere. Plus, many of our students will go on to upper-level courses or graduate studies in which they’ll have to design and interpret their own experiments, and interpret others’ experiments. Anyway, the null hypothesis tests we teach are the usual ones: t-tests, contingency tables, single-factor fixed-effect ANOVA, Tukey’s HSD test, linear regression, correlation. (Note that there’s much more in the course besides the null hypothesis tests I just listed…). In other words, general linear models (GLMs) with at most one predictor variable. We teach all those separate tests because they’re often referred to in the scientific literature, and because that’s the way our textbook (Whitlock & Schluter) teaches them. Students who go on to take advanced biostats learn that everything they learned in intro biostats is just special cases of GLMs. But I increasingly feel like we should just teach GLMs from the get-go in intro biostats, and mention in passing that, by the way, this special case is called “ANOVA” for historical reasons, this other special case is called “regression” for historical reasons, etc. It’s just hard for students to memorize a bunch of separate tests and when to use them. The main things holding us back from switching to a GLM-first approach are (i) the course in its current form seems to work pretty well and we don’t want to fix what isn’t broken (students find it challenging to memorize a bunch of tests, but they do mostly manage it), (ii) it would require a lot of new prep and being a prof is always a matter of triaging demands on one’s time, and (iii) I haven’t found a textbook that I think would work well. Whitlock & Schluter only get to the general case of GLMs in one chapter late in the book. But the GLM-based undergrad biostats textbooks I’ve seen (e.g., Alan Grafen’s book) are too advanced and abstract for our students. Whitlock & Schluter is perfect for our needs in every other way, so the textbook I want is “Whitlock & Schluter, but structured around GLMs from the get-go”. If you know of such a textbook, let me know!

If you wanted to completely revamp the traditional intro biostats course topic list, I think the way to go would probably be towards teaching “intro data science for biology” instead. You’d put a lot more emphasis on data cleaning and processing than we do. More emphasis on exploratory data analysis (though we do teach a bit of that). You’d de-emphasize null hypothesis testing in favor of description and estimation. And you’d familiarize students with some common sorts of observational data biologists work with. Terry McGlynn recently suggested that a course along these lines would be more useful to many biology undergraduates than, say, the traditional calculus or organic chemistry requirements. Curious to hear what readers think of this suggestion.

I think it’d be very hard to get all the way into Bayesian stats in intro biostats. Currently, we don’t even get there in advanced biostats. But I’m sure somebody somewhere does it, and I would be very interested to hear comments on this.

Brian’s answer:

I teach a graduate statistics course (with the only prerequisite being that they have taken a semester stats course at the undergraduate level). So I can tell you a lot about what knowledge student’s actually have coming out of such a course “on average”. But that is not what you asked.

If I were teaching undergraduate stats, I would cover the following:

• Data & experimental design -Types of variables (categorical, binary, ordered, continuous). Responsible data collection. I’ve said more than once that basic experimental design is losing out to advanced stats which is a bad thing. You cannot of course spend the whole course on experimental design. But a week on the difference between observation and experiment, on rigorous control and on breaking confoundment gets pretty far. (1.5 weeks)
• I would definitely teach it using R.That is one big change in the 15 years I’ve been teaching grad stats – when I started literally none of my students knew R coming in. Now 80-90% have already had the basic exposure. And that is thanks to undergrad stats (and other classes) so I wouldn’t break that streak. Have a lab this week on data wrangling, maybe dplyr. Or maybe more time on data visualization and exploration (1 week to get started).
• The general linear model – i.e. the regression with effect size as coefficient view of linear statistics. While I agree with Jeremy that there is no obvious textbook, I just cannot conceive of why I would teach a t-test and anova and a regression separately any more. I would teach up to interaction between categorical variables but probably not ANCOVA (in my experience ANCOVA is something some undergrads have been exposed to and some have not but mostly they don’t understand it regardless so it is probably a bridge too far). And cover a bit of collinearity in multiple regression. This is the heart of the course (5-6 weeks) – ramp them up to be really good at this – diagnostics, interpretation of all R output, contrasts, etc.
• Modes of inference – primarily focusing on NHST/p-values and AIC (both of which I’m not a big fan of but they’re what is used) as well as Bayesian, Monte Carlo/randomization/reshuffling, exploratory (including stepwise) and estimation/confidence intervals. Now you’re not going to teach somebody how to be a Bayesian modeller, but you can get across the conceptual framework. And you can show them how you can view a simple linear regression from each of these frameworks (i.e. show, not have them learn to do). And you might get them to do a simple randomization test (2 weeks)
• Four extensions to the GLM – logistic regression, regression trees, principal component analysis, and a simple site blocking factor as a fixed effect (no way I’m going to mixed models). Purely from a here’s how to do it, not a theory point of view. Just to give them a flavor of what’s coming, not to become experts.  Plus these four techniques get them far into being able to read the literature (2 weeks)

That’s 12.5 weeks which is about the average semester at an American university. If I had more time it would go into more data wrangling, data visualization and the last unit (four extensions). (Jeremy adds: boy howdy, Brian wants to cover material a lot faster than we do in intro biostats at Calgary! He’s proposing to cover a bit more in one course than we cover in 1  course plus 2/3 of a follow-up course! Not criticizing, necessarily–a lot of variables affect how fast you can move through material. Just noting the contrast.)