• South African society emerges from a political legacy that strove to create a dysfunctional society by implementing an official policy of racial discrimination, the effects of which can be observed in education, wealth distribution, employment, and settlement patterns amongst others. Therefore the challenge of statistical literacy is not only improving levels of competency in economics, science and technology, but also to address basic literacy and numeracy. An impressive budget allocation to education in recent times has helped primary education, but less for senior school where drop-out rates are high. An even much bigger challenge is adult illiteracy. In his address to the nation, the President emphasised the need for economic literacy in South Africa. The basic ingredient for this is statistical literacy; thus enhancing knowledge of mathematics and statistics can begin to address deficiencies in economic literacy. While there are programs for training statisticians, this paper confines itself largely to the broad based statistics awareness raising programmes.

  • Since its formation in 1993, the International Association for Statistical Education, IASE, has become a very active international organisation which aims to advance statistical education at all levels, from primary school through training of professionals as well as to the general, public. From 1949 the Committee on Statistical Education within the International Statistical Institute, ISI, promoted the university training of Statisticians at an international level while in developing countries the ISI concerned itself with the education of official statisticians. From the mid 1970's the ISI began to pay more attention to the teaching of statistics at all levels. Since the IASE became a section of the ISI in 1993 it has been responsible for the organisation of all statistical education activities run by the ISI. These include a research group, a range of international meetings, regular publications and a comprehensive website.

  • This paper describes the role of the Royal Statistical Society in shaping statistical education within the UK and further afield. Until 2001 the Society had four agencies concerned with education at all levels. The work of these is discussed and recent new arrangements are outlined. The Society's efforts to disseminate good practice through organising meetings and running a network of Associate Schools and College are explored in some detail.

  • This paper introduces an outline of the integrated web site produced from our faculties' project offering such services as statistics education, electronic books, and statistical libraries for computer use with a search engine for statistics, which we are currently developing. Statistics is a basic and cross-disciplinary study that should be applied to positive analysis in a wide variety of fields. Both the theoretical side and the practical use as a data analysis tool have to be its primary role. Moreover developing statistical analysis software has been the mainstream of disseminating statistics to the various departments. Computers and its network have become quite popular, still use of data processing software such as spreadsheets is expanding among people in the society. In this circumstance, strong demand for basic statistics education is absolutely increasing. In addition as the Internet is supposed to grow as a large-scale communication media, now and in the future, beyond time and space, to increase information about statistical science and education on the Internet promotes the efficient dissemination of statistics.

  • The learning and teaching support network is a programme funded to promote good practice in teaching and learning in UK higher education. Subject networks have been established in twenty-four different areas, including one for mathematics, statistics and operational research. Among all the different activities of this network, the web, of course, offers a rich source of primary material and a convenient means of dissemination. The web provides a vast collection of material on every subject known to man, including statistics. The aim of the ltsn msor web site is to offer a convenient and filtered gateway to a wide variety of teaching and learning material. This paper describes some of the resources available in statistics in particular. Some of the organisational aspects of setting up the website are also mentioned.

  • The availability of comprehensive population registries in Scandinavian countries has facilitated extensive work in epidemiology on associations between risk factors and disease. The area has attracted many statisticians with no previous training in epidemiology. Experience has shown that some statisticians find it difficult to adapt to the practical challenges of this work. Not only is a basic understanding required of the statistical methods involved, but a particular cautious attitude is needed in the interpretation of epidemiological data with inherent uncertainties. An ability to communicate efficiently with coworkers is also essential. Yet the statistician must frequently deal with issues of a biological nature, in addition to technical aspects of data processing. It is difficult to take all these requirements into account in the education of professional statisticians. It is argued that the components not directly connected with statistics should still be integrated into the statistical training of future professionals. If statistics courses include a sufficient amount of relevant data analytic work, the students will be exposed to many of the challenges experienced in epidemiology.

  • In many complex diseases researchers have observed that neither genetic factors nor environmental factors alone determine the disease. This observation generates the hypothesis that human disease is caused by both genetic and environmental factors that act together. This leads to the concept multifactorial causes of disease. On the other hand, the recent compilation of the draft human genome sequence opened the possibility to detect candidate genes for complex diseases and even to study these in relation with environmental factors. The gene-environmental interaction may not be easy to analyze due to the complex structure that the involved factors may have. These factors have different nature that should be treated at different stages of the study. Particular attention should be paid to the study size and design. Epidemiological studies with particular interest in identifying candidate genes that contribute to complex diseases as well as detection of intergenic or gene-environment interactions require large sample sizes because many variables are studied simultaneously. The larger patient populations ensure that individual subgroups retain adequate power to detect significant results with narrow confidence intervals. In the paper we focus on the advantages/disadvantages of classic multifactorial statistical methods applied to the health sciences and the genome scan.

  • Intended both as a textbook for students and as a resource for researchers, this book emphasizes the statistical concepts and assumptions necessary to describe and make inferences about real data. Throughout the book the authors encourage the reader to plot and examine their data, find confidence intervals, use power analyses to determine sample size, and calculate effect sizes. The goal is to ensure the reader understands: the underlying logic and assumptions of the analysis and what it tells them; the limitations of the analysis; and the possible consequences of violating assumptions. The authors adopt a "bottom-up" approach--a simpler, less abstract discussion of analysis of variance is presented prior to developing the more general model. A concern for alternatives to standard analyses allows for the integration of non-parametric techniques into relevant design chapters, rather than in a single, isolated chapter. This organization allows for the comparison of the pros and cons of alternative procedures within the research context to which they apply. Basic concepts such as sampling distributions, expected mean squares, design efficiency and statistical models are emphasized throughout.

  • Several aspects of interview research heretofore receiving little attention are discussed. A brief description of the different types of interview formats and levels of analysis is presented. Following a discussion of the problem of analyzing protocol data, some suggestions are offered about analysis procedures that derive from constructionist assumtions. A model is offered of the interview which describes its role in hypothesis formulation and hypothesis testing. Views on how the interview can be used in combination with other research methods to investigate problem solving are discussed. Finally, how interview research is currently being reported is examined, and recommendations concerning the types of information necessary for inclusion in such reports are offered. Suggestions are aimed at encouraging the researcher to remain skeptical of interpetations of protocol data, and to report the results of interview research fitting specific criticism from the research community. ERIC RIE #ED-204 400.

  • In 1985 the concept of a "DNA fingerprint" was introduced as a means of evaluating human identity and relatednes. (Jeffreys, Wilson, & Thein, 1985). The possible forensic and legal applications of DNA evidence were quickly appreciated and such data are now frequently presented in court cases involving serious crimes such as murder and rape. DNA evidence is also used in establishing paternity, in determining relatedness in immigration and inheritance disputes, and in identifying disaster victims. Such cases, especially those involving famous people, are widely reported in the media and are of interest to the general population. Also, many people will be called to serve on juries in cases where DNA evidence is presented. As statistical concepts are involved in evaluating such evidence, "DNA fingerprinting" as a topic can be used to introduce statistical analysis to undergraduates. If a non-mathematical approach is taken many concepts can be taught to secondary school children, extending their understanding of statistics while holding their interest with practical " real-life" examples.