• The development of a data-driven curriculum for high school mathematics appears to be in line with the needs of students to see more motivation and application within the mathematics classroom and to develop important skills to carry beyond the classroom. The revised curriculum under development is designed to raise the quantitative literacy off all students as it builds connections among mathematics, science, and technology. It models a new approach to the teaching of mathematics, the approach required by the NCTM Standards, as it emphasizes hands-on activities for students and discovery of concepts through data. Technology in the form of graphing calculators and computers is an integral part of the teaching and learning style being promoted through these materials and workshops. This project attempts to connect topics of importance in a modern mathematics curriculum to a modern view of statistical science for the purpose of enhancing student interest and skills in both areas.

  • In 1993 a sample of 102 class members participated in an exploratory study which consisted of ten pairs of statistical questions. For each pair students were asked to specify which of the two options they felt that they would prefer to answer if they were required to or if they had no preference. They were also asked if they could say why they chose particular options. This study clearly indicated that in some ares the students had very definite preferences (70% or more of the group) for particular contexts over others.

  • Computers were invented and constructed to compute. Since statistical analyses are computing intensive it is natural that computers are widely used in statistical research and applications. Statistical applications, such as census with its problems of sorting, counting and tabulating, were among the motives for constructing the ancestors of modern computers. It was always clear that students have to be taught how to use computers, because they will use them in their later careers. With the development of modern technology, computers evolved from large mainframes to personal computers, available for individual use. Availability of personal computers changed the way of computer usage and allowed computers to be incorporated into the teaching process in various disciplines. In particular, can computers be useful in teaching of statistics? If yes, to what extent? What changes in the teaching process are needed if we want to apply computers efficiently? There are many other questions related to the usage of computers in teaching. I would like to present some of my views about computers in teaching of statistics.

  • Video, in the form of broadcast television, is the most popular medium for entertainment and news in the developed world. A television set is one of the first substantial purchases made by households in developing areas as their wealth increases. These phenomena testify to the power of video to hold attention, a power which can also be applied to formal teaching. Video can be used for teaching in several settings: learning at a distance for geographically scattered students, as a supplement in traditional classroom settings, and as a component of new technological learning systems. In each case, wise use of video requires an understanding of both the strengths and weakness, drawing both on practical experience and on cognitive research. We will then suggest appropriate uses of video for teaching statistics in the three settings just mentioned.

  • ConStatS is used in almost all the discipline-specific introductory statistics courses taught at Tufts. Even though most students seem able to use the software for an extended period of time without a great deal of direction, most of the instructors provide assignments that provoke open-ended use of the software. These assignments usually contain short essay questions that address the kinds of conceptual issues captured in ConStatS experiments. During the past year, ConStatS has been at the center of a large scale, FIPSE funded project for evaluating the effectiveness of curricular software. The goal of the evaluation was to assess if ConStatS in particular, and curricular software in general, could help students to develop a deeper conceptual understanding of statistics. The evaluation tool place in the fall 1992 and the spring 1993 semesters. Classes at three universities participated. In total, seven classes with 303 students participated as control groups.

  • This paper discusses the education and training given to participants specializing in Sampling and Statistical Methods at the ISPC. This education and training in survey sampling is conducted through linkage of sampling concepts with the various other survey-related skills and fields. This means, among other things, that it is not sufficient to merely offer a course on sampling theory when the objective is to train participants to be functioning sampling practitioners and technicians. While this paper primarily discusses the training in sampling conducted at the ISPC for statisticians from developing countries, it is nevertheless felt that most of the remarks are relevant to the teaching of survey sampling in general.

  • Sampling techniques may be considered as technology for information production needed for planning socio-economic development. In teaching the sampling course, a balance between sampling theory and its application is required to be maintained. This can be achieved only if a course on design follows practical exercises based on live data. Demonstrations of conduct of actual surveys by involving students from the initial stage of planning to the final stage of reporting the results may be considered as an integral part of the course. A proper sequence in planning the course may be helpful from the communication point of view. Adequate emphasis on the role of sampling and non-sampling errors in interpreting the survey results may be laid. To maximise the utilities of training and research in sampling techniques, there is a need of developing an integrated programme of teaching, research and extension.

  • Basic definitions are given. Then the subject is divided into six "components", which are discussed in turn: I: Planning, design and layout. II: Management of the experiment or experiments. III: Data recording. IV: Scrutiny and editing of the data. V: Computational analysis. VI: Interpreting and reporting the results. Over-theoretical, over-mathematical teaching of the subject is criticized. The importance of practical considerations is stressed.

  • My primary aim in this paper is quite simple. I would like to encourage you to seek out or attempt to discern the main question of interest associated with any given set of data, expressing this question in the (usually non-statistical) terminology of the subject area from whence the data came, before you even think of analysing or modeling the data. Having done this, I would also like to encourage you to view analyses, models etc. simply as means towards the end of providing an answer to the question, where again the answer should be expressed in the terminology of the subject which characterises statistical answers. Finally, and regrettably this last point is by no means superfluous, I would then encourage you to ask yourself whether the answer you gave really did answer the question originally posed, and not some other question. A secondary aim, which I cannot hope to achieve in the time permitted to me, would be to show you how many common difficulties experienced in attempting to draw inferences from data can be resolved by carefully framing the question of interest and the form of answer sought.

  • This address will deal only with Statistical Education in post secondary institutions in Nigeria. The problems of Statistical Education can be put into two categories: (a) Problems that are near universal: Acceptability of Statistics as a distinct area of study; insufficient number of statistics teachers at different levels; balance between theory and practice in teaching statistics. (b) Problems that are culture oriented: remoteness of Africa; inadequacy of modern facilities: books, journals, computers for teaching statistics at various levels. For problems under category (a), our solutions are not any different from the ones already suggested and discussed by many eminent statistics educators.