Original Article
Accessible Conceptions of Statistical Inference: Pulling Ourselves Up by the Bootstraps
Chris J. Wild,
Maxine Pfannkuch,
Matt Regan,
Ross Parsonage,
Chris J. Wild
Department of Statistics, The University of Auckland, Auckland, New Zealand
Search for more papers by this authorMaxine Pfannkuch
Department of Statistics, The University of Auckland, Auckland, New Zealand
Search for more papers by this authorMatt Regan
Department of Statistics, The University of Auckland, Auckland, New Zealand
Search for more papers by this authorRoss Parsonage
Department of Statistics, The University of Auckland, Auckland, New Zealand
Search for more papers by this authorChris J. Wild,
Maxine Pfannkuch,
Matt Regan,
Ross Parsonage,
Chris J. Wild
Department of Statistics, The University of Auckland, Auckland, New Zealand
Search for more papers by this authorMaxine Pfannkuch
Department of Statistics, The University of Auckland, Auckland, New Zealand
Search for more papers by this authorMatt Regan
Department of Statistics, The University of Auckland, Auckland, New Zealand
Search for more papers by this authorRoss Parsonage
Department of Statistics, The University of Auckland, Auckland, New Zealand
Search for more papers by this authorSummary
With the rapid, ongoing expansions in the world of data, we need to devise ways of getting more students much further, much faster. One of the choke points affecting both accessibility to a broad spectrum of students and faster progress is classical statistical inference based on normal theory. In this paper, bootstrap-based confidence intervals and randomisation tests conveyed through dynamic visualisation are developed as a means of reducing cognitive demands and increasing the speed with which application areas can be opened up. We also discuss conceptual pathways and the design of software developed to enable this approach.
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