• Traditional curricula in the social sciences result in students having statistical knowledge that is inert and consequently of low transferability. This is in part because these curricula separate mathematical and probabilistic content (present in statistics service courses) from the context in which the collection of observational and experimental data is designed (present in courses about research methods). This paper proposes a curriculum that removes this separation by merging the two domains into the research competency, in line with emergent pedagogical insights. This study describes the new curriculum and compares some preliminary learning outcomes of students following the proposed integrated competency-based curriculum with that of students following the traditional curriculum. The results suggest a higher level of understanding is achieved through the integrated approach.

  • This study refers to an experiment on teaching probabilities, conducted in Greece at preschools in Athens and Ioannina. The aim of this study was to assess teachers on how they introduced common statistical concepts to children throughout the academic year of 2004-2005. Moreover, this study presents a new didactical model on the way we tried to educate preschool teachers on how to introduce to preschool children probabilistic concepts that are not contained in the official national curriculum. The majority of the teachers agreed that the study was interesting and that their intentions were positive, but they lacked the ability and specification to include those concepts in their everyday class curriculum.

  • Most of the statistical curricula, mainly that written at the elementary level, is based on the classical (frequentist) approach. The Bayesian school, even if originated in the 18th century, has only recently seen a strong development of its tools. This development, however, has not been seen in a basic level. The discipline, as well as the teachers, reflect the classical dominance, which reinforces the current paradigm. Although they have different starting points, both approaches, classical and Bayesian, have tools to analyze data, and we should offer the choice to the student. This article deals with two important concepts, one very useful from the classical point of view, which is the concept of independence, and the other related to the Bayesian thought, the concept of exchangeability. Definitions and simple examples are presented to relate both approaches, from an elementary point of view.

  • The present article concerns statistical concepts that are usually presented in the statistical classroom. Examples are presented in a way such that simple applications of these concepts produce incoherent conclusions. The examples illustrate that: iid random variables are in fact strongly dependent; conditional probabilities may depend on how the conditioning arguments were learned; confidence intervals may have the property of diminished precision when information is increasing; and significance tests may not reject impossible hypotheses.

  • Statistical analysis for social scientists very often means statistical analysis of some questionnaire data. The meaning of the numbers obtained in such analyses depends very much on the kind of scales used. In this paper it is shown that the meaning of numbers can also depend on how exactly the scales are constructed. First, some background information about how scales of the same type (e.g., interval scales) can considerably differ in meaning is given and then a series of study results with interval, ordinal, and nominal scales that demonstrate these differential effects are reported. It is argued that such results can easily be replicated in statistics classes. It is further argued that due to the preponderance of scales in social science research, statistics courses should put an emphasis on teaching the correct use and interpretation of scales. Here, demonstrations such as the ones described in this paper can play a helpful role.

  • Teaching statistics to engineers is a challenging task. First, lacking space, many engineering curricula include few or no statistics courses, and these are often packed and highly theoretical. Thence, students don't perceive statistics as a part of their engineering toolkit, but as a nuisance to endure. On the other hand, engineering is a two-part endeavour. One consists in building or modifying systems. The second is measuring/assessing system performances, which are nothing but random variables. Therefore, there can be no engineering work without statistical analyses. In this paper we discuss and assess ways to enhance the insufficient statistical education that many engineers receive once they have left college. Such methods, designed for practicing professionals include (print and electronic) materials produced for self-study, short training courses and the development of industry-academe organizations to help practicing engineers by "looking over their shoulders." Finally, a selection of related free Web Sites are presented.

  • The Mathematical statistics division at Lund University teaches 8 core and 13 elective statistics courses within 14 different engineering programmes leading to Master of Science in Engineering. This paper uses cases to analyse the combination of ingredients that seems to make the difference between success and otherwise in design of curriculum for engineering programmes. The key aspects underpinning the efforts seem to be collaborative curriculum development, and a joint view from both engineering and statistics of the role of mathematics and statistics in technology and engineering. This respect and high regard for mathematical statistics from the engineering side, often arising from research or other collaboration, changes them from clients to partners. This paper is an attempt to systematize what we see as the important success factors in an engineering statistics education.

  • I report on my experience in consulting with environmental biologists on applications of statistical methods while on a tour of State Nature Reserves and National Parks in Russia, 2003. The biologists that I worked with were well trained in mathematics in comparison to biologists trained in the United States. I also found some biologists with advanced graduate degrees to be well trained in analysis of time series data. However, training in many basic applied statistical methods was either absent, poor, or incorrect. This lack of knowledge of applied statistical methods greatly limits the ability of biologists to design observational studies or manipulate experiments and publish results of their work outside Russia. If there are individuals actively teaching applied statistics in Russia among the audience or among the readers of the proceedings of this conference, I apologize if I have offended anyone. The gap in knowledge of applied statistics is real, however, and I would appreciate communicating with you.

  • Students in medical and health-related fields as well as seasoned medical practitioners need to understand the issues related to the process of planning a scientific study, conducting the study, analysing data and reporting the findings. Recognizing this need, the Iranian medical education system (IMES) started to design and implement a series of workshops in research methodology and statistical data analysis during the past decade. The IMES includes more than 40 universities of medical sciences (UMS). Faculty members and students of the UMS participated in the aforementioned workshops. The workshops provided an opportunity for the participants to get hands-on experiences in the process of scientific research. Based on the above, in the first part of this paper the structure of the workshops, their content and instructional methods are described. Results from a survey to find out the effectiveness of one of the workshop are presented.

  • The objective of this article is to introduce a multidisciplinary biostatistics course program, targeting professionals and academics involved in researching genetics and genomics. The program is designed to adapt statistical concepts, uses and language to this field, which is at the forefront of knowledge. The idea underlying this initiative arose through a research project conducted by physicians and statisticians of the University of São Paulo, Brazil, whose purpose was to identify genetic determinants associated with cardiovascular risk factors in the Brazilian population.