Proceedings

  • The purpose of this paper is to show how the spirit of EDA (Exploratory Data Analysis) may be used at the primary level, the focus being on exploration and stem-and-leaf displays. The paper has three major chapters, one about pupils in primary school, one that deals with student teachers for such pupils, and one about in-service education of teachers who have once been such student teachers. The chapter about pupils begins with a rather lengthy description of a lesson with a grade 3 class. I felt a more abbreviated description could not convey the atmosphere properly, and so I decided to include this fairly complete account of the lesson. The remaining chapters are less detailed and describe some of my experiences of spreading the very basic ideas of EDA to future and practicing elementary teachers. This work is of course necessary when one wishes to introduce EDA in schools.

  • In this paper I will deal with exploratory data analysis, EDA, and will consider EDA to be taught as part of school statistics. Although I think it is important to teach EDA at the school level, I also find it important to teach areas within confirmatory data analysis or statistical inference as well as probability and randomness, but this will not be discussed here. I will first briefly address the issue of who should teach data analysis. Then I will give examples of areas within EDA that are useful to introduce to the mathematics teachers at the upper secondary level.

  • 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.

  • In this paper we focus mainly on the expansion of instruction. Specifically, we examine existing approaches and programs for teaching statistics primarily from the perspective of their ability to reach teachers working with the majority of U.S. students, in "average" or "below average" schools, and who, so far, had made little or no effort to teach statistics. Such teachers often teach the "forgotten halfs" (the 50% or so of U.S. students who do not go to college), the students who may go to college but drop out, and the students who will not be exposed to statistics education after leaving high school. In particular, we examine programs offered to mathematics educators where, ever since the National Council of Teachers of Mathematics has formally included a strand on statistics in its 1989 Curriculum and Evaluation Standards, attempts to introduce statistics into U.S. schools seem to be most visible.

  • The French mathematical community is now convinced that the teaching of mathematics needs a new balance. It is generally agreed that its links to society's needs should be perceived differently, and that the computer can greatly assist in the making of necessary changes. Mathematics educators are conscious that a new challenge has to be faced because of the increasing number of pupils for whom they must cater. In spite of some delay in action that may be attributed to some traditionalist and conservative groups, the new syllabus positively reflects this evolution in approaches to the teaching of mathematics in France.

  • Statistics education has become a significant part of the school mathematics curriculum. Now that it is well established, it is timely to look at some aspects of it to ensure high quality results which involves research in statistics education, as many of us are not aware of the work that is being done. The intention of this paper is to initiate discussion, to establish a research agenda and to use this framework as a means of relating present and desired research activity.

  • This paper offers an overview of research on teaching and learning statistics, what research is needed and for what purpose. The author suggests that future research must concentrate on establishing the best ways to teach statistical concepts so that meaningful long-term learning takes place.

  • We live in an information society. We are confronted, in fact, inundated, with quantitative information at all levels of endeavor, Charts, graphs, figures, rates, percentages, probability, averages, forecasts, trend lines, etc., are in inescapable part of our everyday lives that affect our decisions on health, citizenship, parenthood, jobs, financial concerns and many other important matters. In order to be called for dealing with data and making intelligent decisions based on quantitative arguments. We live in a scientific age. We are confronted with arguments that demand logical, scientific reasoning even if we aren't trained scientists. We must be able to clearly see our way through a maze of reported "facts" in order to separate credible conclusions from specious ones. We must be able to intelligently weigh the evidence on the cause of cancer, the effects of pollutants on the environment, or the results of a limited nuclear war. Teachers, and then students, must be trained to make intelligent decisions based on numerical information if our society is to grow and prosper. We live amidst burgeoning technology. We are confronted with a job market that demands scientific and technological skills, and our students must be trained to deal with the tools of this technology in productive, efficient, and correct ways. Much of this new technology is concerned with information processing and dissemination and proper use of this technology requires statistical skills. These skills are in demand in engineering, business management, data management, and economic forecasting, just to name a few.

  • It is customary for non-statisticians to mock statistical descriptions of social and economic phenomena on the grounds that the statistics can be manipulated to indicate of support one's preconceived views. In other words, they highlight one particular chapter in the textbooks that all of us have read, namely "Uses and Abuses of Statistics". Perhaps we statisticians have invited this ridicule because quantification and hard data are do often used to support "facts", that facts have now become synonymous with statistics. Yet we know, at least in the social sciences, that numbers and measures are vulnerable. Cost of living indices, pure indices, and many other individual and composite numbers, are so value-loaded that we should simply admit the fact: then we would perhaps be less ridiculed. Oxford philosophers have even challenged the existence of anything called a "pure fact", suggesting that no observations are value-free. But however mocking those who do not deal with statistics, there is a deep and genuine unease about statistical descriptions of society and the economy among those who are struggling to remove poverty and inequality. This unease appears at many layers and levels.

  • There remains much scope for statisticians to contribute to the understanding and control of infectious diseases, since much remains unknown about properties of the diseases and their spread. Analysis of infectious disease data presents several challenges to statisticians.

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