Statistics education at the post-secondary level



The discipline of statistics is itself changing in response to vast supplies of data, technological and theoretical advances to tackle more and more complex real problems, and the ready access to powerful open source and commercial software. Students leave post-secondary institutions for a rapidly changing workplace. Statistics graduates need to be prepared for a world of data mining, resampling, Bayesian inference, nonparametric smoothing, computer-intensive techniques, and multivariate contexts. Graduates in other disciplines need to be prepared for workplaces and projects increasingly dependent on statistics and the growing body of statistical methodologies and techniques. All these swirling currents of change and challenge demand a creative revision and reshaping of our curricula and our modes of delivery. We need sustainable approaches that address the reality that Statistics education is vulnerable to backsliding, reductionism and a multiplicity of miscellaneous pressures both intentional and inadvertent.


4ARandomisation and bootstrapping: the quick way to inferenceMichael Forster (New Zealand)
4BUse of student response systems in teaching statistics at the university levelChris Wild (New Zealand)
4CRank-based inference, association measures and nonparametric statisticsIrène Gijbels (Belgium)
Kathryn Prewitt (United States)
4DExchanging pedagogy between post-secondary and secondary school statistics coursesLuke Miratrix (United States)
4EWe know you need to know statistics, do you?Ayse Bilgin (Australia)
4FOpening up the data world wider and fasterJennifer Kaplan (United States)