Literature Index

Ethics play an important role in statistical practice. How can we educate our students about statistical ethics--especially when our courses are already packed with so much...statistics? At the Joint Statistical Meetings in August, I was the discussant in a session on "Teaching Ethics in Statistics Class." First, I will briefly review the points raised by the speakers in that session. George McCabe (Purdue) contrasted the "old" introductory statistics course with its emphasis on methodology to the "new" course. Patricia Humphrey (Georgia Southern) spoke about how she covers ethical data collection in her introductory classes. Paul Velleman (Cornell) talked about the role of judgment in statistical model building and how it makes students (and sometimes us) uncomfortable. I will discuss each of these points in the context of the American Statistical Association's "Ethical Guidelines for Statistical Practice" as well as my own experiences in teaching statistical ethics in an undergraduate capstone course for statistics majors. I will close with an example of statistical ethics in the use of multiple comparison procedures.

The basic principles of experimental design, as it is usually taught today, both to students specialising in statistics and to those in other disciplines, were established nearly fifty years ago. For many statisticians and users of statistics, the major textbook on Design is still Cochran and Cox (1957), written before the advent of the computer. During the last twenty-five years, our computational habits have changed radically, and it is important to ask whether the fundamental ideas of experimental design remain unchanged, or whether these ideas were influenced by the computational environment in which they were developed. I shall not be talking about design by computer, but about the teaching of design liberated by the computer from restrictions imposed by the need to analyse the data. I shall attempt to consider not only the teaching of design now, but into the future.

This paper begins by offering eight recommendations for successful integration of statistics into the pre-college curriculum. Two student-centered activities are described which have been successful in fostering maximum student involvement and involve multiple modes of representation.

Analogical transfer is transfer of a basic structure acquired through one or more instances to another instance. A basic structure like this is sometimes called a paradigm. Paradigmatic teaching, i.e. teaching for analogical transfer, requires the teaching of a basic structure by appropriate exemplars as well as the teaching of its application in various fields and contexts. This is demonstrated using research on teaching for problem-solving, inductive thinking, and learning-to-learn.