Journal Article

  • Both researchers and teachers of statistics have made considerable efforts during the last decades to re-conceptualize statistics courses in accordance with the general reform movement in mathematics education. However, students still hold misconceptions about statistical inference even after following a reformed course. The study presented in this paper addresses the need to further investigate misconceptions about hypothesis tests by (1) documenting which misconceptions are the most common among university students of introductory courses of statistics, and (2) concentrating on an aspect of research about misconceptions that has not yet received much attention thus far, namely the confidence that students have in their misconceptions. Data from 144 college students were collected by means of a questionnaire addressing the most common misconceptions found in the literature about the definitions of hypothesis test, p-value, and significance level. In this questionnaire, students were asked to select a level of confidence in their responses (from 0 to 10) for each item. A considerable number of participants seemed to hold misconceptions and lower levels of concept-specific self-perceived efficacy were found to be related to misconceptions more than to the correct answers. On average, students selected significantly lower levels of confidence for the question addressing the definition of the significance level than for the other two items. Suggestions for further research and practice that emerge from this study are proposed.

  • Previous research has demonstrated that students' cognitions about statistics are related to their performance in statistics assessments. The purpose of this research is to examine the nature of the relationships between undergraduate psychology students' previous experiences of maths, statistics and computing; their attitudes toward statistics; and assessment on a statistics course. Of the variables examined, the strongest predictor of assessment outcome was students' attitude about their intellectual knowledge and skills in relation to statistics at the end of the statistics curriculum. This attitude was related to students' perceptions of their maths ability at the beginning of the statistics curriculum. Interventions could be designed to change such attitudes with the aim of improving students' learning of statistics.

  • This paper describes a unique graduate-level course that prepares teachers of introductory statistics at the college and high school levels. The course was developed as part of a graduate degree program in statistics education. Although originally taught in a face-to-face setting, the class has been converted to an online course to be accessible to more students. The course serves students who are pursuing graduate degrees in a variety of disciplines but who want to teach statistics as part of their careers. It also serves current teachers in high school who are teaching the Advanced Placement Statistics course as well as teachers at two-year and four-year colleges. The curriculum for the course is based on the theory that good teachers of statistics need to be developed, as opposed to being trained. Building on recent teacher preparation theory, we describe a course that models and builds specific knowledge about teaching and learning statistics. In addition, this course is organized around the six recommendations of the ASA-endorsed Guidelines for Assessment and Instruction in Statistics Education (GAISE).

  • Group activities are an excellent way to enhance learning. When students are actively involved in a relevant project, understanding and retention are improved. The proposed activity introduces a timely and interesting project typical of the type encountered in statistical practice. Using the computer to successfully developing an appropriate model is a valuable educational experience that builds confidence.

  • In a statistics course for bachelor students in econometrics a new format was adopted in which students were encouraged to study more actively and in which cooperative learning and peer teaching was implemented. Students had to work in groups of two or three students where each group had to perform certain tasks. One of these tasks was: explaining theory and/or solutions of problems to the other groups. In order to prepare them for this task the groups had separate regular meetings with the teacher. Students report higher involvement and greater satisfaction in this format than in the traditional format. For the teacher the format may be more time consuming, but also more rewarding.

  • Hypothesis testing is one of the more difficult concepts for students to master in a basic, undergraduate statistics course. Students often are puzzled as to why statisticians simply don't calculate the probability that a hypothesis is true. This article presents an exercise that forces students to lay out on their own a procedure for testing a hypothesis. The result is that the students develop a better understanding for the rationale and process of hypothesis testing. As a consequence, they improve their ability to grasp the meaning of a p-value and to interpret the results of a significance test.

  • Following the Guidelines for Assessment and Instruction in Statistics Education (GAISE) recommendation to use real data, an example is presented in which simple linear regression is used to evaluate the effect of the Montreal Protocol on atmospheric concentration of chlorofluorocarbons. This simple set of data, obtained from a public archive, can be used to tell a compelling story of success in international diplomacy solving a global environmental problem. A description of the use of these data and analyses are presented for a number of courses in applied statistics including introductory statistics.

  • In response to the worldwide shortage of biostatisticians, Australia has established a national consortium of eight universities to develop and deliver a Masters program in biostatistics. This article describes our successful innovative multi-institutional training model, which may be of value to other countries. We first present the issues confronting the future of biostatistics in Australia, then relate our experience in establishing a new national consortium-based Masters program, and finally explore the extent to which our initiatives have addressed the current challenges of biostatistics workforce shortages.

  • Students increasingly need to learn to communicate statistical results clearly and effectively, as well as to become competent consumers of statistical information. These two learning goals are particularly important for business students. In line with reform movements in Statistics Education and the GAISE guidelines, we are working to implement teaching strategies and assessment methods that align instruction and assessment with our learning goals. One of the main instructional tools we use is group projects with elements of data collection and analysis, written and oral presentation, and self, peer and professor assessment. This paper addresses specific challenges encountered while teaching and directing group work in a highly multicultural context of 10 to 20 different nationalities in the same classroom. It also focuses on the learning benefits of having students work collaboratively to discuss, write, present, and assess statistics projects in English.

  • This paper describes an interactive activity that revolves around the dice-based golf game GOLO.<br>The GOLO game can be purchased at various retail locations or online at igolo.com. In addition, the game may be played online free of charge at igolo.com. The activity is completed in four parts. The four parts can be used in a sequence or they can be used individually. Part 1 illustrates the binomial distribution. Part 2 illustrates the sampling distribution of the sample proportion. Part 3 illustrates confidence intervals for a population proportion. Part 4 illustrates hypothesis tests for a population proportion.<br>Extensions of the activity can be used to illustrate discrete probability distributions (including the geometric, hypergeometric, and negative binomial) and the distribution of the first order statistic. The activity can be used in an AP statistics course or an introductory undergraduate statistics course. The extensions of the activity can be used in an intermediate undergraduate statistics course or a mathematical statistics course. Each extension is self-contained and can be carried out without having worked through other extensions or any of the four parts of the main activity.

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