Abstract
It is the year 2014. Despite advances in technology and the availability of interactive software and teaching tools, it is still the norm to use normality-based methods, and even look-up tables, to teach statistical inference. This puts a heavy burden on our students, who must struggle through difficult theory before they’re taught how to make decisions and draw inferences from data. Even then, do they understand what a p-value is or what a confidence interval represents? Is there a better way? Interactive computer simulations and resampling methods can help bridge the gap between graphs and summary statistics and inference, providing a gentler and more natural transition. Until recently, these methods required add-ins, specialized programs or custom code. Today, these techniques are available in mainstream statistical software. In this talk, we illustrate how to use simulations, bootstrapping, and randomization tests in JMP® to introduce sampling distributions and explore core inferential concepts.