Literature Index

Advancing technology is inexorably shifting the demand for statisticians from being operators of mechanical procedures to being thinkers. Coupled with this is a perceived lack of development of statistical thinking in students. This chapter discusses the thought processes involved in statistical problem solving in the broad sense from problem formulation to conclusions. It draws on the literature and in-depth interviews, with statistics students and practising statisticians, which aimed at uncovering their statistical reasoning processes. From these interviews from all four exploratory studies, a four dimensional statistical thinking framework for empirical enquiry has been identified. It includes an investigative cycle, an interrogative cycle, types of thinking and dispositions. There are a number of associated elements such as techniques for thinking and constraints on thinking. The characterisation of these processes through models, that can be used as a basis for thinking tools or frameworks for the enhancement of problem-solving, is begun in this chapter. Tools of this form would complement the mathematical models used in analysis. The tools would also address areas of the process of statistical investigation that the mathematical models do not, particularly in areas requiring the synthesis of problem-contextual and statistical understanding. The central element of published definitions of statistical thinking is<br>"variation." The role of variation in the statistical conception of real-world problems, including the search for causes, is further discussed.

Models for statistical modes of thinking and problem solving have been developed, and continue to be developed, by teachers and researchers. The purpose of these models range from helping to understand how individual students solve problems to developing instruments for educational research. These models have arisen with particular perspectives and primary uses in mind. In this paper we compare and contrast some statistical thinking models originating from statistics education research (Ben-Zvi &amp; Friedlander, 1997; Jones, Thornton, Langrall, Mooney, Perry &amp; Putt, 2000) with some models arising from the discipline of statistics and sub-disciplines (Wild &amp; Pfannkuch, 1999; Hoerl &amp; Snee, 2001). Drawing upon models from both these areas we discuss issues that include their development and use, how they might illuminate one another and what we can learn from them.

Identifies and describes students' variation-type thinking observed in a small sample of middle-school students as they conducted a statistical investigation.

This paper reviews the current status of teaching both undergraduate and post-graduate doctors in the UK. An example is given of the way both areas are covered at the University of Sheffield UK. The use of topical subjects to interest students is described. The future of teaching medical statistics, the way it may be delivered and its links with Evidence Based Medicine are discussed.