Teaching

  • A two-year clinical research curriculum offered in a graduate program at a U.S. chiropractic college was implemented in Fall 2003 and enrolls three to six chiropractors per year. The curriculum includes ten credit hours of required courses in biostatistics. Introductory courses in biostatistical thinking and reasoning and data management are offered the first term, followed by basic statistical methods, statistical graphics, and advanced topics over the next three terms. Trainees typically have little previous exposure to statistics, so program objectives move from developing critical appraisal skills to writing strong data-related sections in grant applications. As graduates will likely pursue careers at chiropractic colleges with little or no research infrastructure, nor necessarily a research culture, it is paramount they develop a strong foundation in research methods and become proficient users of statistical tools to succeed.

  • Statistical consulting not only provides real life examples to mention in class; it provides a reality check that influences the way we teach and the choice of topics to teach or emphasize. The focus of this paper is on topics that are frequently omitted or not emphasized but which we consider important as a direct result of consulting experiences. The first four belong to introductory courses and among them are data issues, the test of hypothesis about proportions for small samples using the binomial distribution and some topics on categorical data analysis. The last two topics, one on statistical models and the other in time series, belong to upper division courses.

  • Education in methods of applied statistics is important for students who will be involved in management and decision-making processes. This paper discusses issues related to the teaching of statistics to students enrolled in an undergraduate environmental science degree course. The aim is to describe the teaching of graphical and numerical methods for summarizing and exploring data obtained in environmental studies. The application of descriptive and exploratory methods provides useful information regarding the distribution of the data at hand and of its patterns and associations. These methods are presented at the beginning of the course, following an introduction to the steps involved in the process of learning from data through the use of statistics. Students are instructed in the reading and interpretation of graphic and numeric data summary techniques. The importance of visualizing the main patterns and associations in the data is emphasized using environmental examples.

  • Geostatistics is sometimes a difficult leap for even those individuals well versed in classical statistics. The impact of data location in spatial statistics may be only vaguely understood initially. Visualization tools that allow the student or practitioner to see the impact of moving data, adding additional data, deleting data, adding fault lines, changing search radiuses, and so forth aid the learning of geostatistical concepts. Due to page limitations only a few items are briefly illustrated. This visualization software called the Kriging Game is available free at http://geoecosse.bizland.com/softwares/ . This site also has other free geostatistical software and tutorials.

  • One of the biggest challenges statisticians face when working with non-statisticians on applied problems is to be able to effectively communicate the statistical results. In this paper we discuss the use of interactive visualization as a tool to present the relationship between a binary response and a set of explanatory variables. The visualization system we present allows users to "manipulate" directly, dynamically, and interactively their data set. At a first level, this allows to integrate visualization with a classical statistical analysis by providing interactive 3D views of the data set. Beyond its potential use as a straightforward visualization tool, this new system opens up interesting possibilities for exploring data visually, by its better exploitation of the human visual system. The paper presents an example of exploring visual relationships between environmental variables and the presence/absence of Lyme disease in Rhode Island.

  • This paper illustrates by means of an important European survey on earnings how user-friendly interactive visualisation tools can be applied to communicate results of official statistics and to connect official statistics to the world of statistics education. The visualisation tools presented are self-contained with built-in methodological comments. They can be used offline on a CD-ROM or as part of dynamic PowerPoint presentations. They might be likewise used online on the Web sites of statistical offices, possibly embedded in virtual libraries, or as an integral part of electronic publications.

  • This paper considers how New Zealand journalists report political polls. Two recent newspaper articles are featured. Perhaps not surprisingly we have detected a tendency for journalists to focus on sample size, to misunderstand the concept of margins of error, and to have little idea as to whether a result is generalisable. We also consider the importance of non-respondents. We wonder if journalists question the validity of survey results they have been given. We ask the question: could a "non-random" convenience survey have as much validity as a more formal survey conducted by a specialist research company?

  • This paper describes a course that was developed to teach statistics to students majoring in Psychology and Politics. There were several interesting aspects to this course. Firstly each lecture contained between 550 and 800 students. Secondly those students were almost uniformly negatively disposed to Statistics prior to the beginning of the course. Thirdly we were required to provide an introduction to Statistics in just 12 lectures, each of 50 minutes duration. Constrained, we were forced to think deeply about what we want to provide to students in an Introductory Statistics course. Making use of simulations and the internet, we chose to emphasise concepts and critical thinking and supported these with examples which had direct relevance to our students. Restricted to 12 lectures, we learned to make optimum use of each lecture. Can a short course like this act as a useful pre-cursor to the standard Introductory Statistics course?

  • Conditional probability and Bayesian reasoning are important to psychology students because they are involved in the understanding of classical and Bayesian inference, regression and correlation, linear models, multivariate analysis and other statistical procedures that are often used in psychological research. A study of previous literature showed that there is considerable research on this topic, but no comprehensive questionnaires have been developed to globally assess students' understanding and misconceptions on these topics. At the University of Granada we started building a questionnaire, which takes into account the content of conditional probability taught in the Spanish universities to psychology students, as well as the biases and misconceptions described in the literature. In this work we will describe the process of developing the questionnaire and will report the results from a sample of 206 psychology students.

  • Several editorial and institutional interventions in psychology have aimed to improve statistical reporting in journals. These efforts have sought to de-emphasise statistical significance and encourage alternative analyses, especially effect sizes and confidence intervals (CIs), but the interventions to date have had short-lived and superficial impact-if any impact at all. I review some of these interventions in psychology and discuss possible reasons for lack of success. I give an inter-disciplinary context by discussing reform efforts in medicine-in which useful reform has already been achieved-and ecology. I then identify statistics education as the next major challenge for reformers, and report data on students' understanding of CIs, and difficulties they have making appropriate interpretation of CIs. I explain the need for further evidence on which to base improved statistics education in psychology.

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