• The present book focuses on the intersection between two recent lines of thought. Both have been called "revolutions." The term "cognitive revolution" signifies the transition from understanding humans in terms of overt behavior to understanding them in terms of mental structures and processes. The term "probabilistic revolution" describes the transition from a deterministic understanding of science, in which uncertainty and variability were not permitted, to an understanding of science where probabilistic ideas became indispensable in theorizing. From the intersection of the inference revolution and the rising cognitive revolution a new understanding of the mind emerged: The mind as an "intuitive statistician." It is this second revolution on which the book focuses: It treats the new view of cognitive processes as statistical inference and hypotheses testing. But claim that the success of the second revolution relies heavily on that of the first and that the new methods of inferences have been transplanted to serve as explanations for how many cognitive processes work., and this has brought to cognitive psychology both a unifying perspective and, also certain blind spots inherent in these institutionalized statistical tools.

  • New goals for learning probability are very different from traditional computational objectives and include heavy emphasis on integration with other topics in mathematics and with other subject areas. Although there is no solid knowledge of how to teach probability ideas well, new materials in a variety of forms are attempting to draw on recent research on students' intuition.

  • This paper will concentrate on thinking and communicating. No attempt, however, is made here to define a "knowledge syllabus" of conceptual and philosophical understandings, methodological or problem solving skills, or required studies of societal impacts for a course.

  • This session concerns using technology to improve instruction. My part of this topic deals with television, a decidedly old technology that is better known among college teachers for damaging the preparation of students than for instructing them.

  • In order to educate high school students in statistical reasoning, we have developed, under National Science Foundation support, a computer-enhanced curriculum called Reasoning Under Uncertainty and microcomputer software called ELASTIC. The curriculum emphasizes reasoning and learning-by-doing as methods for helping students understand the hows and whys of statistics. The software is built on design principles of interactivity, visualization, and multiple, linked representations; it provides a laboratory in which students can explore the underlying meaning of abstract statistical concepts and processes. This paper describes the innovative aspects of the software and curriculum and the results of a field test in two high school classrooms- one urban, one suburban.

  • There are many reasons why computers should be used in our courses. Still they are underutilized in many statistics classes today. In this paper the author will address each of the possible reasons why this is so. He will conclude with some thoughts about the future of computers in our courses.

  • In this work we want to underline the fact that the use of the statistical tool in experimental and social sciences, in general, and in the didactics of mathematics in particular is converted, in this field of knowledge, in a specific object of the study, due to the mathematical nature of the concepts and to the didactic processes implied.

  • Statistics are pervasive in our society yet the understanding of statistics has remained the domain of a select few. Although the majority of the literature has focused on the adult learner, there is a movement towards teaching statistics to childern. This paper addresses the ways in which statistics has been examined in the elementary and secondary schools in terms of content, readiness to learn, pedagogy, and assessment. We conclude with a proposal for how a cognitive apprenticeship model can be developed from the empirical research findings to build more effective instructional and assessment methods for statistics education. Further empirical work may highlight other components of statistical proficiency that should be modeled for learners.

  • Strong movements in both education research and education reform are emphasizing that teaching should encourage student activity rather than simply aim knowledge in the general direction of a student audience. Yet video, at least in its traditional technological forms, is passive. How can teachers make effective use of an apparently in effective medium? What role can video best play in new multimedia instructional systems? This article reviews research on learning through television in order to make practical suggestions. Specific examples are two widely distributed series, Against All Odds: Inside Statistics and Statistics: Decisions Through Data.

  • We present a critique showing the flawed logical structure of statistical significance tests. We then attempt to analyze, why, in spite of this faulty reasoning the use of significance tests persists. We identify the illusion of probabilistic proof by contradiction as a central stumbling block, because it is based on a misleading generalization of reasoning from logic to inference under uncertainty. We present new data from a student sample and examples from the psychological literature showing the strength and prevalence of this illusion. We identify some intrinsic cognitive mechanisms (similarity to modus tollens reasoning; verbal ambiguity in describing the meaning of significance tests; and the need to rule out chance findings) and extrinsic social pressures which help to maintain the illusion. We conclude by mentioning some alternative methods for presenting and analyzing psychological data, none of which can be considered the ultimate method.