Literature Index

Students in elementary statistics traditionally see experiments and data as words and numbers in a text. They receive little exposure to the important statistical activities of sample selection, data collection, experimental design, development of statistical models, the need for randomization, selection of factors, etc. They often leave the first course without a firm understanding of the role of applied statistics or of the statistician in scientific investigations. In an attempt to improve elementary statistics education, we have developed a statistics laboratory similar to those of other elementary science courses. We will discuss our experiences in teaching the one-semester hour Elementary Statistics Laboratory course that can be taken with or after the traditional elementary statistics course. In each session students, working in teams, discuss the design of an experiment, carry out the experiment, and analyze their data using Minitab on a Macintosh. The students then individually either answer a series of short answer questions or write a formal scientific report. The labs are designed to be relatively inexpensive and do not require a prior background in science, statistics or computing.

The purpose of the present study was to identify the areas in graphical representations that elementary teachers fixated on, as well as, their alternative conceptions of several statistical concepts. The specific research questions were: 1) What features of a line plot and histogram do elementary teachers fixate on when interpreting data? 2) What alternative conceptions do elementary teachers have about center or middle of the data, typical, and prediction when interpreting graphical representations of data?

This paper is part of a research study, the objective of which is to investigate the meaning of confidence intervals for first year students in the Statistics course in the Universidad Nacional de Rosario. Within the framework of Godino's theory (1999), by means of students' dialogues in front of the computer, we try to surmise the presence of the diverse elements of meaning - extensive, ostensive, actuating, intensive and validating - which reveal the topic comprehension. Statistical inference is one of the largest branches of Statistics and a fundamental methodological tool in the empiric sciences, in particular. It allows us to quantify our confidence in conclusions drawn from random samples, and therefore, to verify our impressions by means of calculations (Batanero, 2001); hence, the importance of an updated teaching.

The interconnected themes of quality and the marketing of the discipline of statistics are explored. An understanding of statistics as the study of the process of scientific inquiry is advocated as a consciously targeted market position. Because it reaches such a high proportion of the managers and decision makers of the future, the introductory university or college statistics course is highlighted as a potent marketing opportunity for enhancing the long-term health of statistics. Attention is given to teaching students to think statistically, to become educated consumers of statistical expertise, and to communicate well with nonstatisticians.