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HELPING STUDENTS FIND THEIR STATISTICAL VOICES

Communication in statistics is multifaceted, and is involved in all phases of statistical analysis and modelling: planning, collecting, handling, exploring and discussing data; articulation, synthesis and development of probabilistic/statistical models; understanding and applying concepts; and using, interpreting and reporting results of, formal statistical analyses in context. An important thread throughout, for students in their learning, and for statisticians in communicating, is connecting with everyday and "already-owned" concepts in probability and data. This paper discusses and analyses strategies that have been integrated throughout some introductory tertiary courses to help students develop their communication in, and of statistics - to themselves, with others, and in carrying out all aspects of statistical data investigations, analyses, interpreting and reporting. INTRODUCTION This paper focuses on some key communication aspects of introductory tertiary statistics courses, mostly introductory data analysis courses, but also introductory probability and distribution courses that include some statistical/stochastic modelling. Introductory data analysis courses have received much attention in the statistical education literature in the past decade, associated with the universality of data analysis across many disciplines. Topics have included issues of maths anxiety; emphasis on real contexts and data; effects of technology; and the increased inclusion of data across all school levels. For these reasons, plus the extent of the learning and teaching impact of the own-choice group project strategy in data analysis (MacGillivray, 1998; 2002a), this paper focuses particularly on the communication aspects of the introductory data analysis types of courses across a range of disciplines, including prospective statistics majors. However, emphasis on data should not prevent awareness of the importance of communication in probability and probability modelling. The push to emphasise statistical thinking through the use of more data (for example, Moore, 1997), with fewer recipes and derivations, along with the automation of computations and graphics, is now the norm for many introductory tertiary data analysis courses. Issues that inform discussion of communication in, and of, statistics include analysis of statistical reasoning and statistical thinking (Watson & Callingham, 2003; Wild & Pfannkuch, 1999); assessment practices that assess what is valued and that help students to develop understanding of themselves as individual learners (Angelo, 1999; Chance, 2002); and student engagement ( James, McInnis, & Devlin, 2002).