Next week I am giving a two-hour talk and discussion for Kingston University researchers and doctoral students, with the aim of being an update on statistics for those who are not active in the field. That’s an interesting and quite challenging mission, not least of all because it must fit into two hours, with the first hour being an overview for newcomers like PhD students from health and social care disciplines, and the second hour looking at big current topics. I thought I would cover these points in the second half:
- crisis of replication: what does it mean for researchers, and how is “good practice” likely to change?
- GAISE, curriculum reform & simulation in teaching
- data visualization
- big data
- machine learning
The first half warrants a revised version of this handout, with the talk then structuring the ideas around three traditions of teaching and learning stats:
- classical, mathematically grounded, stats, exemplified by Snedecor, Fisher, Neyman & Pearson, and many textbooks with either a theoretical or applied focus. Likelihood and/or adding prior to get posterior distributions are the big concepts here.
- cookbook, exemplified by many popular textbooks out there, especially if their titles make light of statistics as a ‘hard’ subject (you could count Fisher here as the first evangelical writer in 1925, though it is harsh to put him in the same camp as some of these flimsy contemporary textbooks)
- reformist, exemplified by Tukey in the 70s but consolidated around George Cobb and Joan Garfield’s work for the American Statistical Association. The only books for this are “Statistics: Unlocking the Power of Data” by the Lock family and “Introduction to Statistical Investigations” by Tintle et al.
It’s worth remembering that there are other great thinkers who accept the role of computational thinking and yet insist that you can’t really do statistics without being skilled in mathematics, of whom David Cox springs to mind.
The topics to interweave with those three traditions are models, sampling distribution versus data distribution, likelihood, significance testing as a historic aide to hand calculation, and Bayesian principles. I’ll put slides on my website when they’re ready.
While I’m on this subject, I’ll tell you about an afternoon meeting at the Royal Statistical Society on 13 October, which I have organised. The topic is making computational thinking part of learning statistics, and we have three great speakers: Helen Drury (Mathematics Mastery) representing the schools perspective, Kari Lock Morgan (Penn State University) representing the university perspective, and Jim Ridgway (University of Durham) considering what the profession should do about the changing face of teaching our subject.