Literature Index

The findings reported in this article came from a study which took place in an introductory college-level statistics course and which adopted a nontraditional approach to statistics instruction that had variation as its central tenet. The conjecture driving the study was that poor understanding of statistical concepts might be the result of instructional neglect of variation and that instruction which puts emphasis on building student intuitions about variation and its relevance to statistics should also lead to improved comprehension of other statistical concepts. The results of the study point to a number of critical junctures and obstacles to the conceptual evolution of variation. The article discusses one of those critical junctures and obstacles, the understanding of histograms.

What is it that makes the difference for students? What is their perspective on what benefited or hindered their learning in a first statistics class? Additionally, can students' reasons be categorized based on different student characteristics or learning styles? To answer these questions, the present investigation is a qualitative follow-up to a quantitative analysis that was aimed at determining relationships among learning styles, academic programs, background variables and attitude toward statistics.

This paper provides an example of how student-centered instruction can be used in a theoretical statistics class. The author taught a two-semester undergraduate probability and mathematical statistics sequence using primarily teacher-centered instruction in the first semester and primarily student-centered instruction in the second semester. A subset of the students in the teacher-centered course also took the student-centered course. Student feedback suggests that the student-centered approach, while more difficult for both student and instructor, is beneficial when compared to the teacher-centered approach. The specific method of implementation will need to vary with class size and level of student preparation but the author's example presents a starting point for those interested in moving away from a traditional teaching approach in theoretical statistics classes.

In describing the work of the nineteenth-century statistician Quetelet, Porter (1986)<br>suggested that his major contribution was in:<br><br>...persuading some illustrious successors of the advantage that could be gained in certain cases by turning attention away from the concrete causes of individual phenomena and concentrating instead on the statistical information presented by the larger whole (pg. 55).<br><br>This observation describes the essence of a statistical perspective - attending to features of aggregates as opposed to features of individuals. In attending to where a collection of values is entered and how those values are distributed, statistics deals for the most part with features belonging not to any of the individual elements, but to the aggregate which they comprise. While statistical assertions such as "50% of marriages in the U.S. result in divorce" or "the life expectancy of women born in the U.S. is 78.3 years" might be used to make individual forecasts, they are more typically interpreted as group tendencies or propensities. In this article, we raise the possibility that some of the difficulty people have in formulating and interpreting statistical arguments results from their not having adopted such a perspective, and that they make sense of statistics by interpreting them using more familiar, but inappropriate, comparison schemes.