Conference Paper

  • 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.

  • When instruction in statistical concepts can be tied to practical sports issues, students are motivated to understand the statistical concepts. In this paper we describe an issue that would be relevant to discussions of many different sports leagues, and would also be a vehicle for teaching statistical concepts such as simulation, graphical displays, illusions of randomness, measurement of variability, and the logic of hypothesis testing. In addition to motivating a keen interest in the effects of random variation, these examples provide students with a way to verbalize what they learn in statistics classes to their lay acquaintances. Moreover, examples like these have the potential for engaging instructors who have been focused on more traditional approaches. Programs in the software language R are provided and their use with introductory classes is discussed.

  • We discuss our initial experience with offering a version of our standard introductory statistics course that focused primarily on sports related examples, rather than a more traditional selection of applications. This special sports section was offered in parallel with a regular section, covering the same statistical topics, with the same instructor, at the same pace. We examine how the students enrolling in the sports section might differ from the regular, illustrate how we converted material from the regular section to the sports equivalent, compare the performance of students between the two sections and reflect on the effectiveness of the sports-based approach.

  • Sports data are commonly used to present topics from introductory statistics, such as exploratory data analysis and probability. They also can illustrate more subtle and complex statistical issues, such as selecting appropriate variables, making casual inferences from observational data, and specifying appropriate inferential populations. In this paper, I discuss how sports data can be used to engage students on such fundamental aspects of data analysis. I frame the discussion around the question posed in the title, a question which has generated much debate among baseball enthusiasts.

  • Statistics plays a leading role in finance. The explosive development of increasingly complex markets makes it more and more difficult for practitioners to correctly value financial asset. Statistical analysis has become a powerful tool for a better market valuation, taking a leading role in the development of new financial products that try to hedge the increasing amount of risks that an investor has to take. Statistics knowledge demand is steadily increasing in Hedge Funds, Investment Banking and Financial Institutions in general, where statistics students could developed a professional career. Finance can be seen as a way to motivate students on the applications of almost any statistical tool we would like to teach them, since we could always find an example where these techniques are put into practice.

  • The Actuarial profession appeals to many with excellent quantitative skills who aspire to "make financial sense of the future." However the road to qualification as an Actuary through the Institute or Faculty of Actuaries in the UK (or the Society of Actuaries or Casualty Actuary Society in the United States) is not an easy one, and a series of very challenging exams must be passed to qualify as a Fellow Actuary. These exams test many skills, and in particular demand a good knowledge of probability and statistics. The main areas of work for actuaries are traditionally life assurance, actuarial consultancy, general insurance, and investment. Although statistical skills are required in all of these areas, they are particularly important in general insurance. In this paper we discuss the basic tools and techniques in probability and statistics that are essential for an actuary who intends to work in general insurance.

  • In this paper, we present a brief history of our efforts to incorporate civic learning into our statistics curriculum, highlighting our most recent approach, media reports. We discuss implementation issues, educational objectives, and give examples of student projects. Learning objectives, expected outcomes, and our assessment process are also given. An important aspect of this effort is the use of technology in report generation and dissemination. We discuss the development of these tools and how they have been used. We conclude with remarks on sustainability and possible future directions.

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