• Statistical literacy is a key ability expected of citizens in information-laden societies, and is often touted as an expected outcome of schooling and as a necessary component of adults' numeracy and literacy. Yet, its meaning and building blocks have received little explicit attention. This chapter proposes a conceptualization of statistical literacy and describes its key components. Statistical literacy is portrayed as the ability to interpret, critically evaluate, and communicate about statistical information and messages. It is argued that statistically literate behavior is predicated on the joint activation of five interrelated knowledge bases (literacy, statistical, mathematical, context, and critical), together with a cluster of supporting dispositions and enabling beliefs. Educational and research implications are discussed, and responsibilities facing educators, statisticians, and other stakeholders are outlined.

  • Statistical questions suffuse the favric of our society at almost all folds. When Bill Krusak and I offered that ovservation in an Amstat News artical just over a decade ago, the universe of our immediate concern was the federal statistical system--a universe that to some may have seemed rather parochial. Our principal intent in sharing out views with the members of ASA was to underscore the pervasiveness of statistics produced by the federal govenment in out professional and personal lives. The urgency in our voices stemmed from what we perceived to be "penny-wise but poiund0foolish decision" that would undermine the quality of data for research, program planning, allocation of resources, and policy evaluation--by academics, buiness leaders, government officials, and citizens--for years to come (Druskal and Wallman 1982).<br>It is not my mission tonight to revisit either historic or recent tragedies and triumphs of the federal statisital system. Many have written and spoken on these matters; several of my predecessors have discussed these issues, and how the ASA might respond to them, intheir presidential addresses to out membershp. I will, hoever, use the milieu of federal statistic as the opening scene for elaborating my hope that by enhancing statistical literacy we may succeed in enriching our society. My aims for the remarks I will share with you this evening are three:<br>--to underscore the importance of strengthening understanding of statistics and statistical thinking among all sectors of our population;<br>--to highlight some avenue we can pursue to enhance our citizens' statistical literacy; and<br>--to suggest some ways that individual statisticians and the American Statistical Association can enrich our society.

  • Clarifies how students' mathematical reasoning as acts of participation are analyzed in the mathematical practices established by the classroom community. Presents episodes from a recently completed classroom teaching experiment that focused on statistics. Discusses change, diversity, and equity.

  • Determining the main research questions in statistics education is not an easy task, because there are so many important and unanswered questions relating to the teaching and learning of statistics. Nevertheless, in SERN 1 (2) we proposed a list of questions that we considered important to investigate, given the current state of research in statistics education as well as our own ideas and research traditions. We reflected on the diversity of people involved in statistics education research, the difficulties of having access to the literature in this area, and the challenges of training statistics education researchers within different disciplines. Our short note was complemented in SERN 2(1) by reactions from a number of colleagues from different countries who represent different backgrounds and experiences. These differences as well as the interdisciplinary nature of statistics education research were visible in the variety of responses and suggestions in the written responses. In this rejoinder, we attempt to synthesise the main points raised by the different reactors to whom we are very grateful, as they provided many important complementary ideas. It would be a too big a task to reply in detail to each of the points raised as some of them deserve a full issue of the Newsletter. We are therefore only offering remarks here regarding a few of the main points raised. We plan to focus on some of the remaining topics in future issues of our Newsletter.

  • The history and current nature of research in statistics education are outlined and some<br>suggestions for its future direction are made. It is claimed that research in statistics<br>education is a research discipline in its own right.

  • Reflecting on a body of research work can sometimes lead to the recognition of areas of opportunity for research that have gone largely unnoticed. In this paper we consider three such opportunities in the area of research on the teaching and learnig of probability and statistics: i) Following up on students' initial thinking to watch for future transitions; ii) Investigating students' thinking on variability; and iii) Posing research questions that begin with what students can do rather than pointing out what they cannot do. Situations from research tasks, past and future, are used as starting points for the discussion.

  • As teachers of statistics we know the fundamental components of statistical enquiry, be it classical or exploratory. When we turn the focus on ourselves as statistics educators, we run the risk of forgetting some of the fundamental principles of good research - principles that are broader than carrying out statistical significance tests. In this talk I want to present some examples of research in statistics education to illustrate the stages and outcomes that contribute to results that have a scholarly impact on the statistics education community. As a single teacher with a good idea on how to teach "confidence intervals," I do not expect anyone to pay much attention to me. If I can, however, place my ideas in the context of others' ideas or research on teaching confidence intervals; conduct a study - maybe a case study or a controlled experimental<br>design - that is valid for considering the issue I want to promote in teaching about confidence intervals; and have my results refereed by peers in the field; then I can expect people to pay attention to me.

  • Improving the public's understanding of statistical information requires that producers or reporters of statistical messages are aware of: The nature of people's statistics literacy, The factors that affect the difficulty of statistics-related messages, The existence of individual or group differences in statistics literacy; and The information needs of different target audiences. Implications are discussed regarding the need to prepare different types of communicative products and formulate strategies for dissemination and public education

  • Over many years I have been attempting to improve statistical literacy in the population by changing the school curriculum. All such attempts have to be put in the general context of teaching, learning and assessing the subject. Ideally these should complement and reinforce each other. In practice they often conflict - in particular assessment can distort the learning process. In this talk I consider the nature of these conflicts and how they might be overcome in practice, giving examples from a lifetime's experience.

  • The development of a Profile of Statistical Understanding is aimed at providing a tool to assist educators to identify what 'can be' expected of students rather than what 'should be'. This profile needs to cover all basic areas of statistics in such a way that specific profiles will identify what to expect of graduating secondary students with respect to 'statistical understanding' for tertiary education or the work force. Responses from 13 to 18 year-olds to open-ended questions were analyzed using the SOLO model as a framework for an hierarchy. The profile is presented and responses from typical students are discussed to elaborate on the categories. Rasch analysis combined the rankings of students on different questions to produce a measure of statistical understanding for each student. A profile for an average student is discussed