Questionable Claims for Simple Versions of the Bootstrap

A survey of introductory statistics courses at Japanese medical schools was published as a report in 2014. To obtain a complete understanding of the way in which statistics is taught at the university level in Japan, it is important to extend this survey to related fields, including pharmacy, dentistry, and nursing. The current study investigates the introductory statistics courses offered by faculties of pharmaceutical sciences (six-year programs) at Japanese universities, comparing the features of these courses with those studied in the survey of medical schools. We collected relevant data from the online syllabi of statistics courses published on the websites of 71 universities. The survey items included basic course information (for example, the course names, the targeted student grades, the number of credits, and course classification), textbooks, handouts, the doctoral subject and employment status of each lecturer, and course contents. The period surveyed was July-September 2015. We found that these 71 universities offered a total of 128 statistics courses. There were 67 course names, the most common of which was "biostatistics (iryou toukeigaku)." About half of the courses were designed for first- or second-year students. Students earned fewer than two credits. There were 62 different types of textbooks. The lecturers held doctoral degrees in 18 different subjects, the most common being a doctorate in pharmacy or science. Some course content differed, reflecting the lecturers' academic specialties. The content of introductory statistics courses taught in pharmaceutical science programs also differed slightly from the equivalent content taught in medical schools.

Best (2001) asserts that “all statistics are socially constructed.” This paper investigates three questions. (1) Is Best’s thesis true? (2) If so, should this fact be taught in introductory statistics courses? (3) If so, what general principles involving the social construction of statistics can be taught that don’t rely on detailed knowledge of a particular subject? Isaacson (2011) notes that the social construction of statistics is readily presented by asking students “Where do statistics come from?” Results of a small survey show that statistical educators are seriously divided on whether this topic should be taught. Examples of questions and principles used in teaching Statistical Literacy at Augsburg College are presented. This paper argues that the Isaacson question, “Where do statistics come from?” is fundamental to statistical education and should be at the center of any course titled “statistical literacy”. If there isn’t time to teach this material in introductory statistics courses, then statistical educators should support offering a separate statistical literacy course in order to cover this important topic.

ABSTRACTMany students enrolled in first year introductory statistics courses believe learning statistics is a waste of time and fear they will fail. In this study, we explored the impacts on learning outcomes for students in an introductory statistics course by allowing students to arbitrarily choose their own sequence of learning from three key learning activities, namely tutorials, Peer-Assisted Study Sessions and Computer-Managed Learning quizzes. Unlike the old regime where the learning activities followed a strict, rigid sequence, a new regime allowed students to freely choose when, where and how they engaged with the course learning activities. This allowed increased opportunities for students to receive relevant and timely feedback. Using a total of 1187 students enrolled in semester 2 of 2011, 2012 and 2013, data were collected on students’ scores from 7 assessment tasks. Our experimental design ensured as many course features as possible remained constant between the control cohorts (of 2011 and 2012) and the experimental cohort (2013), thereby avoiding potential sample selection problems. The findings showed student learning outcomes in the new regime improved significantly. Interestingly, the effects were found to be greatest in the lower percentile of the score distribution.

We read the recent article, Statistics From Whom? in Teaching Sociology (Caine et al., 1978) with interest since our university (Eastern New Mexico University) had recently examined the same question-who should teach the introductory course in statistics? Caine et al. answered the question, through argument, that an introductory statistics course should be taught within the department of sociology by a sociologist with a statistics specialty. Their perspective is that each academic area is unique and thus requires their own statistics course. Before responding directly to the principal question-who would teach the introductory course in statistics?-we digress briefly to consider two related questions: At any university where a department of statistics exists and statistically trained specialists also exist within other departments, what statistics courses should be taught and who should teach them? We feel that the statistics department should teach service courses stressing techniques that are used in many areas of application (and,

Many undergraduate degrees require the completion of an introductory statistics course, but it is unclear to what extent taking a statistics course improves statistical literacy. We conducted an online survey with a simple random sample of undergraduate students at the University of Waterloo, Canada. We then compared students who have completed at least one statistics course to those who have completed none and found that taking a statistics course did not improve statistical literacy on the questions asked (causation versus correlation, margin of error, and others). Introductory statistics courses may want to address statistical literacy as a learning outcome more explicitly for a better understanding of public policies.

The majority of undergraduate introductory statistics courses emphasize that statistical insights are gleaned from hypothesis testing. 1 The hypothesis testing approach is procedure-oriented: The appropriate statistical test must be used to fit one’s circumstance, and these different hypothesis tests are (approximately) distinct and non-overlapping. Introductory statistics courses often begin by introducing central tendency and variability and then move to sampling distributions and probability distributions, forming the building blocks to support hypothesis tests and then presenting the back-of-the-book tables that support hypothesis testing decision-making. The typical undergraduate statistics course then spends the rest of the semester introducing the various statistical tests and teaching students how to figure out which to apply in relation to various research designs.

Examining interactions between baseline expectancy beliefs and task values on weekly motivation in an introductory statistics course

Community colleges play vital roles in higher education by providing affordable and accessible educational opportunities for learners from diverse backgrounds. To meet the high demand for post-secondary educational needs, community colleges rely on online education to offer this core service to their expanding student body. Few studies have examined which demographic factors and learning engagement measures, as measured by learning analytics tools, impact students' successful course completion in online introductory statistics courses at community colleges. This quantitative study first aimed to explore and examine how learner demographic factors/characteristics impact academic performance and successful outcomes in an online introductory statistics course at a community college. Then, I will investigate learners' learning engagement and interaction patterns, such as time spent on assignments, and how these measures lead to successful course completion outcomes and performances in an online introductory statistics course at a community college. Finally, examine which demographic factors have a moderating effect on these engagement measures. Data for the study was obtained from administrative sources, including students' academic records and performance analytics data on students' interactions with the digital learning platform MyLab Math® or MyLab Statistics®. Findings from this study suggested that female students, Federal Pell Grant recipients, and students who were single parents when enrolling in the course had lower odds of completing the course successfully. These findings can guide instructional designers, instructors, and administrators at the community college to go beyond the flexibility and convenience offered by these online introductory mathematics/statistics to provide target resources to improve the chances of these vulnerable students succeeding in these "gatekeeper" online courses. These findings also provide an avenue for more research into understanding the online learning engagement of at-risk students and provide ways to motivate and encourage students to spend more time and effort completing assigned academic tasks, improving their chances of success in online introductory mathematics and statistics courses.

Course projects have been argued to help develop students’ statistical thinking, but implementing authentic and realistic course projects still presents major challenges. This paper evaluated students’ experiences and perceptions of using an online simulated virtual environment, known as the Island, for implementing major course projects within an online masters level introductory statistics course. The use of the Island aimed to overcome significant practical and ethical constraints imposed on project-based work in online courses. The project required students to answer a self-posed research question by gathering and analysing data using methods covered in the course. The project was divided into two parts, a mid-semester proposal and an end of semester online presentation. Following completion of the projects, forty-two students responded to a questionnaire which rated their level of agreement to three aspects of using the Island: engagement, ease of use and contributes to understanding. Students were also asked to provide qualitative comments and five students participated in semi-structured interviews. Qualitative feedback was analysed to help explain the results from the quantitative questionnaire. In conclusion, perceptions of the use of the Island for project-based assessment were very positive. Qualitative feedback provided insight into how the Island-based projects may help to develop students’ statistical thinking.

This paper evaluated students’ experiences and perceptions of using an online simulated virtual environment, known as the Island, for project-based work within an online master’s level introductory statistics course. Forty-two students responded to an Island questionnaire which rated their level of agreement to three aspects of using the Island - engagement, ease of use and contributes to understanding. Students were also asked to provide qualitative comments and five students participated in semi-structured interviews. Qualitative feedback was analysed to help explain the results from the quantitative questionnaire. In conclusion, perceptions of the use of the Island for project-based assessment were very positive. Qualitative feedback provided insight into how the Island may help to develop students’ statistical thinking.

The ability to conduct and correctly interpret the results of hypothesis tests is one of the most important skills that students can acquire in the introductory statistics course. Unfortunately, it is also one of the most difficult skills for them to learn.' Although this problem is widely recognized, textbook authors disagree about the best approach to use for conducting one-tailed hypothesis tests. In a recent survey of introductory business and economics statistics textbooks, we found that about half use the simple null hypothesis approach and about half use the composite null hypothesis approach for one-tailed hypothesis tests.2 Whereas both approaches are valid, the composite null hypothesis approach requires a more detailed statistical explanation than does the simple null hypothesis approach. Unfortunately, most textbooks that use the composite null hypothesis approach eschew the relevant explanation and use, instead, an explanation suitable only for the simple null hypothesis approach.

New Undergraduate Degree in Statistics at the University of Missouri

In this study we examined the effects of prior mathematics achievement and completion of a commercially developed, National Science Foundation-funded, or University of Chicago School Mathematics Project high school mathematics curriculum on achievement in students’ first college statistics course. Specifically, we examined the relationship between students’ high school mathematics achievement and high school mathematics curriculum on the difficulty level of students’ first college statistics course, and on the grade earned in that course. In general, students with greater prior mathematics achievement took more difficult statistics courses and earned higher grades in those courses. The high school mathematics curriculum a student completed was unrelated to statistics grades and course-taking. First published May 2012 at Statistics Education Research Journal: Archives

Over the past decade, software has found its way into the curriculum for a variety of educational purposes. This has been especially true in the case of introductory statistics courses. There are at least two principal uses for technology in such introductory courses. The first is to free students of excessive computational responsibilities, thereby enabling them to spend more time analyzing data and assessing results. The second is to help students to get a deeper understanding of the fundamental concepts taught in an introductory course, thereby putting the students in a better position to use powerful tool-bases software and to interpret results. In a sense, both of these uses have the same aim, namely to improve the general quantitative reasoning skills of the students. In many respects this is the greatest challenge of an introductory statistics course, or of any cause with a large quantitate component.

This study evaluates the effectiveness of two different computer assisted instruction (CAI) formats compared with traditional in-class instruction as a laboratory supplement to lectures in introductory statistics. The labs occurred during the first third of the course and dealt with descriptive statistics. One hundred and thirty-four students enrolled in an introductory statistics course in psychology were assigned to one of the three lab sessions. The evaluation consisted of affective responses to a questionnaire concerning the lab session, as well as student performance on three homework assignments and on a midterm examination. Lab format had a significant effect on attitude toward the lab; it did not, however, significantly influence performance. Path analysis revealed interesting relations between lab format, student attitude toward the lab, and performance on the midterm examination. These findings point to the need to further examine CAI as a supplement to introductory statistics instruction.