Literature Index

A university's introductory statistics course was redesigned to incorporate technology (including a website) and to implement a standards-based approach that would parallel the recent standards-based education mandate for the state's K-12 schools. The author collected some attitude (pre and post) and performance (post only) data from the "treatment" section and two "comparison (i.e., more traditional)" sections. There was a pattern of positive attitude towards the redesigned aspects of the course, including group work, lab and project emphasis, criterion-referenced assessment and examples from real-life. On the three problems given to the three sections at the end of the course, the only significant ANOVA (F(2,101) = 4.2, p = .0168) involved the treatment section scoring higher than the other sections. This occurred on a problem involving critical thinking (with a graphic from USA Today), an emphasis supported by the particular standards of the redesigned course.

Language and the telling of data stories have fundamental roles in advancing the GAISE <br><br>agenda of shifting the emphasis in statistics education from the operation of sets of <br><br>procedures towards conceptual understanding and communication. In this paper we discuss <br><br>some of the major issues surrounding story telling in statistics, challenge current practices, <br><br>open debates about what constitutes good verbalization of structure in graphical and<br><br>numerical summaries, and attempt to clarify what underlying concepts should be brought to <br><br>students  attention, and how. Narrowing in on the particular problem of comparing groups, <br><br>we propose that instead of simply reading and interpreting coded information from graphs, <br><br>students should engage in understanding and verbalizing the rich conceptual repertoire <br><br>underpinning comparisons using plots. These essential data-dialogues include paying <br><br>attention to language, invoking descriptive and inferential thoughts, and determining <br><br>informally whether claims can be made about the underlying populations from the sample <br><br>data. A detailed teacher guide on comparative reasoning is presented and discussed.

We first note that tests for interaction are missing in virtually all textbooks on nonparametric statistics. We will discuss some reasons why this is so. We then make a case for featuring tests for interaction in the course. By learning how to use median polish and graphical displays students can begin to conceptualize what an interaction means. This will strengthen their understanding of additive models as well. After a conceptual basis for understanding interaction is in place, we can then proceed to design tests for interaction. They will not be strictly nonparametric. This provides a good opportunity for discussion of what it means to have a nonparametric test and why it is impossible to construct an ordinary permutation test for interaction.

Basic probability concepts are difficult for some students to understand initially. Through the use of a Venn diagram disguised as a pizza, we will discuss how to explain introductory probability concepts. Students are able to answer probability questions, including conditional probability, by simply looking at a picture. This tool not only enhances learning but retention as well.