ABSTRACTTo incorporate active learning and cooperative teamwork in statistics classroom, this article introduces a creative three-dimensional educational tool and an in-class activity designed for introducing the topic of agglomerative hierarchical clustering. The educational tool consists of a simple bulletin board and color pushpins (it can also be realized with a less expensive alternative) based on which students work collaboratively in small groups of 3–5 to complete the task of agglomerative hierarchical clustering: they start with n singleton clusters, each corresponding to a pushpin of a unique color on the board, and work step by step to merge all pushpins into one single cluster using the single linkage, complete linkage, or group average linkage criteria. We present a detailed lesson plan that accompanies the designed activity and also provide a real data example in the supplementary materials. Supplementary materials for this article are available online.

Biostatistics is a critical skill to physicians in an evidence-based medicine era, but teaching basic statistical concepts is challenging. Students often experience anxiety caused by the complexity of statistics and might express negative attitudes toward the subject. We aimed to analyze the effect of an introductory biostatistics course using RStudio on attitude toward statistics and assess its acceptance among medical students. Forty-three 1st-year medical students were included. Pre- and post-course attitudes toward statistics were assessed using the Survey of Attitudes Toward Statistics (SATS-28) scale and technology acceptance was assessed by a Technology Acceptance Model scale at the end of the course. There was a statistically discernible (significant) gain in the scores of three of the four SATS dimensions: affection (p = 0.006, Cohen’s d = 0.442), cognitive competence (p < 0.001, Cohen’s d = 0.605), and difficulty (p = 0.008, Cohen’s d = 0.421). Acceptance of RStudio was moderate to high in 93% of the participants, without statistical differences between genders. RStudio can be useful in the teaching of statistics to medical students, being well accepted and positively associated with students’ attitude toward statistics. Supplementary files for this article are available online.

Recent years have seen increasing interest in incorporating resampling methods into introductory statistics courses and the high school mathematics curriculum. While the use of permutation tests for data from experiments is a step forward, the use of simple bootstrap methods for sampling situations is more problematical. This article demonstrates via counterexamples that many of the claims made for these simple methods are simply wrong. Their use with beginners can only be justified after their true properties have been fully researched, and their many limitations explained to students. Supplementary materials for this article are available online.

Instructors of postsecondary classes in statistics rely heavily on visuals in their teaching, both within the classroom and in resources like textbooks, handouts, and software, but this information is often inaccessible to students who are blind or visually impaired (BVI). The unique challenges involved in adapting both pedagogy and course materials to accommodate a BVI student may provoke anxiety among instructors teaching a BVI student for the first time, and instructors may end up feeling unprepared or “reinventing the wheel.” We discuss a wide variety of accommodations inside and outside of the classroom grounded in the empirical literature on cognition and learning and informed by our own experience teaching a blind student in an introductory statistics course.

Data science is a discipline that provides principles, methodology, and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many academic institutions have started to offer degrees in data science, with many at the graduate, and a few at the undergraduate level. Curricula may differ at different institutions, because of varying levels of faculty expertise, and different disciplines (such as mathematics, computer science, and business) in developing the curriculum. The University of Massachusetts Dartmouth started offering degree programs in data science from Fall 2015, at both the undergraduate and the graduate level. Quite a few articles have been published that deal with graduate data science courses, much less so dealing with undergraduate ones. Our discussion will focus on undergraduate course structure and function, and specifically, a first course in data science. Our design of this course centers around a concept called the data science life cycle. That is, we view tasks or steps in the practice of data science as forming a process, consisting of states that indicate how it comes into life, how different tasks in data science depend on or interact with others until the birth of a data product or a conclusion. Naturally, different pieces of the data science life cycle then form individual parts of the course. Details of each piece are filled up by concepts, techniques, or skills that are popular in industry. Consequently, the design of our course is both “principled” and practical. A significant feature of our course philosophy is that, in line with activity theory, the course is based on the use of tools to transform real data to answer strongly motivated questions related to the data.

AR: Thanks very much, Nick, for agreeing to be interviewed for the Journal of Statistics Education. I’m anxious to hear your thoughts on statistics education in the present, but let’s start a few y...

ABSTRACTIn this article, a large and rich dataset from the U.S. Small Business Administration (SBA) and an accompanying assignment designed to teach statistics as an investigative process of decision making are presented. Guidelines for the assignment titled “Should This Loan Be Approved or Denied?,” along with a subset of the larger dataset, are provided. For this case-study assignment, students assume the role of loan officer at a bank and are asked to approve or deny a loan by assessing its risk of default using logistic regression. Since this assignment is designed for introductory business statistic courses, additional methods for more advanced data analysis courses are also suggested.

ABSTRACTIn this article, we revisit the café story first introduced in 2011. Recent data from this café run by business students at a Midwestern public university are explored and analyzed. The data were collected using a point-of-sale system over a 3-month period during the spring semester of 2015. These data can be used in introductory statistics courses to illustrate the use of time series and forecasting, applications of data mining and visualization, as well as sampling, confidence intervals, and inference using ANOVA and chi-square tests for independence. Since the data pertain to a student-run business, we believe that statistics students, especially those in business disciplines, will find the data's context to be relevant and interesting. In addition to the technical exercises, we provide background and context for several managerial issues that these data can be used to address, thus emphasizing the importance of data-driven decision making.