Literature Index

This article expresses concern for the sloppy language that is often used by the media to describe numerical comparisons and suggests guidelines for how such comparisons should be described such that the meanings are unambiguous<br>This article expresses concern for the sloppy language that is often used by the media to describe numerical comparisons and suggests guidelines for how such comparisons should be described such that the meanings are unambiguous.<br><br>This article expresses concern for the sloppy language that is often used by the media to describe numerical comparisons and suggests guidelines for how such comparisons should be described such that the meanings are unambiguous.<br><br>This article expresses concern for the sloppy language that is often used by the media to describe numerical comparisons and suggests guidelines for how such comparisons should be described such that the meanings are unambiguous.<br><br>This article expresses concern for the sloppy language that is often used by the media to describe numerical comparisons and suggests guidelines for how such comparisons should be described such that the meanings are unambiguous.<br><br>This article expresses concern for the sloppy language that is often used by the media to describe numerical comparisons and suggests guidelines for how such comparisons should be described such that the meanings are unambiguous.<br><br>This article expresses concern for the sloppy language that is often used by the media to describe numerical comparisons and suggests guidelines for how such comparisons should be described such that the meanings are unambiguous.

In this paper we summarize the research we have recently carried out on classifying problems<br>of conditional probability. We investigate a particular world of school word problems we call ternary<br>problems of conditional probability. With the help of a mathematical object, the trinomial graph, and the<br>analysis and synthesis method, we propose a framework for a structural, didactical and phenomenological<br>analysis of the ternary problems of conditional probability. Consequently, we have organized this world into<br>several types of problems. With respect to students' behaviour, we identify four types of thinking processes<br>related to data format and the use of data. We also illustrate our approach by use of the diagnostic test<br>situation, and in the particular context of health.<br>The main purpose of our work is to improve secondary school students' understanding of conditional<br>probability and their probability literacy by proposing a teaching approach based on problem solving within<br>appropriate contexts. We believe that the framework we present in this paper could help teachers and<br>researchers in this purpose.

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 enquiry 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 non-statisticians.

Many students do not understand what representational problems a particular notation solves, thus limiting their ability to use the notation, as well as their understanding of the problem situation it applies to. Forty-six undergraduates completed a lesson designed to help them understand variance and its notation. Students in the invention group were asked to create a procedure for calculating the variance of contrasting distributions of numbers; students in the procedural group were presented with a procedure for calculating variance and asked to practice it on the numbers. Results indicate that invention students learned to reflect on the quantitative properties of distributions, and to evaluate statistical procedures in terms of their ability to differentiate those properties. Students in the procedural condition tended to evaluate a procedure simply in terms of whether or not it was like the "correct" procedure. We plan to extend this instructional method to facilitate classroom conversations and as a platform for a complementary intelligent instructional system.