Most statistics educators would agree that statistical inference is both the central objective of statistical reasoning and one of the most difficult ideas for students to understand. In traditional approaches, statistical inference is introduced as a quantitative problem, usually of figuring out the probability of obtaining an observed result on the assumption that the null hypothesis is true. In this article, we lay out an alternative approach towards teaching statistical inference that we are calling "informal inference." We begin by describing informal inference and then illustrate ways we have been trying to develop the component ideas of informal inference in a recent data analysis seminar with teachers; our particular emphasis in this article is on the ways in which teachers used TinkerPlots, a statistical visualization tool. After describing teachers' approaches to an inferential task, we offer some preliminary hypotheses about the conceptual issues that arose for them.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education