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The Statistics Teaching Inventory (STI) was designed to assess the teaching practices of U.S.-based, college-level introductory statistics instructors in a variety of institutions and departments. This instrument has now been updated to reflect current trends and recommendations in statistics education. In this study, we used the STI to examine the current state of the curricular and instructional practices being used by U.S.-based, college-level introductory statistics instructors. We explore the extent to which instructors report that their introductory statistics courses are aligned with recommended practices as outlined by the 2016 GAISE College Report. Data were collected from a sample of college-level U.S.-based, college-level introductory statistics instructors. Results based on 228 usable responses indicated that instructors, by-and-large, tended to be following the GAISE recommendations, especially related to content. However, courses may not yet be aligned with newer content recommendations (e.g., provide students opportunities to work with multivariate data), and there is still a large percentage of instructors that are not embracing student-oriented pedagogies and assessment methods. for this article are available online. [ABSTRACT FROM AUTHOR]

Nontraditional grading methods have recently become more common, and as with any large pedagogical shift, there are a number of questions to consider when applying a new grading scheme to a course. This article summarizes four types of nontraditional grading and shares experiences from the authors who have applied them to a variety of courses in statistics. This article is structured as a set of questions and answers, seeking to address many of the concerns and considerations that one may face as they transition a course's grading structure. for this article are available online. [ABSTRACT FROM AUTHOR]

This article introduces a collection of four datasets, similar to Anscombe's quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an included factor. The article includes a mathematical summary of each dataset, as well as directed acyclic graphs that depict the relationships between the variables. Despite the fact that the statistical summaries and visualizations for each dataset are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example datasets can help practitioners gain a better understanding of the assumptions underlying causal inference methods and emphasize the importance of gathering more information beyond what can be obtained from statistical tools alone. The article also includes R code for reproducing all figures and provides access to the datasets themselves through an R package named "quartets." for this article are available online. [ABSTRACT FROM AUTHOR]

In recent years, the discipline of statistics has begun reckoning with its difficult history. Institutions are reconsidering names that have honored key historical figures in statistics who have deep ties to eugenics movements and racial and class prejudice. These names, however, continue to appear in our classrooms, where we teach the methods created by these individuals, raising the question of how instructors should address their legacies. Three examples of famous statisticians and their work—Francis Galton's use of conditional probabilities to demonstrate "hereditary talent," Karl Pearson's attempt to quantify the intelligence of Jewish immigrant students, and Ronald A. Fisher's creation of the analysis of variance to de-emphasize environment in human development—highlight the intimate ties between statistics and eugenics. These examples, along with a discussion of the context of these men, eugenics movements, and the statisticians and scientists who opposed their eugenic programs, can humanize the field for students, teach them about the challenges in accurate and unbiased data collection and analysis, and connect historical mistakes to contemporary ethical issues. Confronting this history in the classroom can both improve the teaching of the statistical methodologies themselves and begin a broader conversation about the role of statistics in the world. for this article are available online. [ABSTRACT FROM AUTHOR]

An anonymous survey was given to n = 73 students in an asynchronous online statistical literacy course at a mid-sized Hispanic Serving Institution. Informed by teaching experience, literature on lexical ambiguity, and everyday usage of statistics words and phrases, the first author designed the survey to yield insight into how students view phrases such as "average person," "random person," and "outlier person," and to explore possible connections or patterns with such phrases. Findings suggest that students view phrases such as "random person" in a way much further from standard usage than they do phrases such as "anonymous person." Considerations such as diversity, agency, and variable identification are identified as possibly affecting usage and meriting further investigation. Recommendations for teaching and future research are provided. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

In this article, we explore the drivers of students' success in a first-year university statistics course. Using a unique sample from Westminster International University in Tashkent, we discover that student engagement with their studies is reflected in their class attendance and in the use of online resources, which continue to play an important role in their academic performance. Moreover, we discovered that socio-demographic factors such as age, gender, marital status, employment, and language of instruction of prior schooling are significant in explaining student academic outcomes. These factors have important policy implications for both the university and the broader national level. [ABSTRACT FROM AUTHOR]

In many collegiate level statistics courses, the focus of the learning outcomes is often on the analysis of data after it has been collected. Students are provided with clean data sets from previous studies to practice statistical analysis, but receive little to no application as to the amount of time and effort that goes in to collecting good data. To account for these deficits at the author's institution, a design of experiments course was created that provided students with a more hands-on learning experience to the statistical process, especially as pertains to data collection. This paper focuses on five of the experiments that students designed and implemented during the course, and some suggestions to instructors that may wish to use these experiments in their own courses. [ABSTRACT FROM AUTHOR]

Can a "nudge" toward engaging, fun, and useful material improve student attitudes toward statistics? We report on the results of a randomized study to assess the effect of a "nudge" delivered via a weekly E-mail digest on the attitudes of students enrolled in a large introductory statistics course taught in both flipped and fully online formats. Students were randomized to receive either a personalized weekly E-mail digest with course information and a "nudge" to read and explore interesting applications of statistics relevant to the weekly course material, or a generic course E-mail digest with the same course information, and no "nudge." Our study found no evidence that "nudging" students to read and explore interesting applications of statistics resulted in better attitudes toward statistics. for this article are available online. [ABSTRACT FROM AUTHOR]

According to decades of research in educational psychology, learning is a social process that is enhanced when it happens in contexts that are familiar and relevant. But because of the skyrocketing popularity of data science, today we often work with students coming from an abundance of academic concentrations, professional, and personal backgrounds. How can our teaching account for the existing multiplicity of interests and be inclusive of diverse cultural, socioeconomic, and professional backgrounds? Music is a convenient medium that can engage and include. Enter Playmeans, a novel web application ("app") that enables students to perform unsupervised learning while exploring music. The flexible user interface lets a student select their favorite artist and acquire, in real time, the corresponding discography in a matter of seconds. The student then interacts with the acquired data by means of visualizing, clustering, and, most importantly, listening to music—all of which are happening within the novel Playmeans app. for this article are available online. [ABSTRACT FROM AUTHOR]

Being a teacher or a student in a class with a large enrollment can be intimidating. Often, teachers view comforts that are common to small classes as unattainable in a larger class, including knowing students' names, using active learning, employing group work, and creating group discussion. Students in large classes may find that the class size leads to isolation. At Utah State University, we offer introductory statistics classes for various audiences using a large lecture format. The authors have collectively led these large lectures dozens of times and found that, despite its shortcomings, the large lecture format can be an asset when teaching statistics. With an active learning approach such as recommended by the revised GAISE College report, large class sizes permit realistic sampling, facilitate student-driven simulation, and provide bountiful opportunities for experimentation. In this article, we discuss the benefits of large classes for statistics teaching and present examples of using large class sizes to create an engaging environment that where students are involved in active learning and collecting real data to foster statistical thinking. [ABSTRACT FROM AUTHOR]

In K–12 statistics education, there is a call to integrate statistics content standards throughout a mathematics curriculum and to teach these standards from a data analytic perspective. Annotated lesson notes within a lesson plan are a freely available resource to provide teachers support when navigating potentially unfamiliar statistics content and teaching practices. We identified several types of annotated lesson notes, created two statistics lesson plans that contained various annotated lesson notes, and observed secondary mathematics teachers implement the lesson plans in their intermediate algebra courses. For this study, we qualitatively investigated how two teachers' instructional actions compared to what was prescribed in the annotated lesson notes. We found ways in which the teachers' instructional actions, across their differing contexts, aligned with, varied from, or adapted to the annotated lesson notes. From these results, we highlight affordances and limitations of annotated lesson notes for statistics instruction and offer recommendations for those who create statistics curricula with annotated lesson notes. [ABSTRACT FROM AUTHOR]

The Journal of Statistics and Data Science Education regularly publishes interviews with notable statistics and data science educators. These interviews aim to provide insight into the development of statistics and data science education, offer a historical perspective, and humanize the field through personal anecdotes. Allan Rossman has been conducting these interviews since 2011, profiling a diverse range of individuals who have made significant contributions to the field. Taylor & Francis has made the complete set of interviews easily accessible through centralized lists. The current issue of the journal features papers on various topics, including the role of eugenics in the development of statistics and strategies for addressing this history in the classroom. [Extracted from the article]

Statistical literacy is key in this heavily polarized information age for an informed and critical citizenry to make sense of arguments in the media and society. The responsibility of developing statistical literacy is often left to the K-12 mathematics curriculum. In this article, we discuss our investigation of K-8 students' current opportunities to learn statistics created by state mathematics standards. We analyze the standards for alignment to the Guidelines for the Assessment and Instruction in Statistics Education (GAISE II) PreK-12 report and summarize the conceptual themes that emerged. We found that while states provide K-8 students opportunities to analyze and interpret data, they do not offer many opportunities for students to engage in formulating questions and collecting/considering data. We discuss the implications of the findings for policy makers and researchers and provide recommendations for policy makers and standards writers. [ABSTRACT FROM AUTHOR]

The widely used Draw a Scientist Test was revised to focus on statistics and 110 Elementary Statistics students were asked to draw a statistician. In addition, to better understand students' drawings and have some relative comparison, 173 College Algebra students were asked to draw a mathematician. A detailed analysis of students' images and students' demographic information was conducted using descriptive statistics, categorical data analysis, logistic regressions, and hierarchical cluster analysis. Results showed that students tend to perceive statisticians and mathematicians as primarily White and male. However, female students were more likely than male students to draw a female statistician and mathematician. Two themed clusters emerged from the hierarchical cluster analysis for both the math and statistics students. We discuss the implications of the results for teaching and future research. [ABSTRACT FROM AUTHOR]

A Statistics Practicum course is offered as another option besides an internship or research experience for students to fulfill a required statistics major capstone experience. This article discusses the first and fourth offering of this practicum course, which provides a unique perspective on the initial implementation of the course and its development over time. The course offers students opportunities to carry out statistical consulting projects with external clients. Students were given multiple reflection assignments throughout the course. Challenges of the projects were discussed in the reflections, which included issues of data cleaning and analysis. Students also responded to both Likert-scale and open-ended questions on an end of semester survey. These responses provided information on sentiment regarding the consulting projects and perceived usefulness of various components of the Statistics Practicum course. Both student reflection assignments and survey responses were analyzed in this study. Explanations of the thought processes that went into setting up and running the course are included. Advice and suggestions for course improvements and successful administration are also presented. [ABSTRACT FROM AUTHOR]

For ease of instruction in the classroom, the one-way analysis of variance F statistic is rewritten in terms of pairwise differences in individual samplemeans instead of differences of individual samplemeans from the overall samplemean. Likewise, the Kruskal-Wallis statisticmay be rewritten in terms of pairwise differences in individual average ranks rather than differences of individual average ranks from the overall average rank. In unbalanced designs, it is seen that the contribution to either test statistic from a pair of samples is related to the product of the sample sizes multiplied by the square of the relevant pairwise difference. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Statisticians and data scientists have been called upon to increase the impact they have through their collaborative projects. Statistics and data science practitioners and their educators can achieve and enable greater impact by learning how to create shared understanding with their collaborators as well as teaching this concept to their students, colleagues, and mentees. In this article, we explore and explain the concepts of commonknowledge and shared understanding, which is the basis for action to accomplish greater impacts. We also explore related concepts of misunderstanding and doubtful understanding. We describe a process for teaching oneself and others how to create shared understanding. We conclude that incorporating the concept of shared understanding into one's practice of statistics or data science and following the steps described will result in having more impact on projects and throughout one's career. [ABSTRACT FROM AUTHOR]

The Chicago Hardship Index is a proposed starting point for introducing students to structural urban inequities. ASA's mission statement to use statistics to enhance human welfare serves as a motivation for social justice projects. This article contains an application of ASA's ethical guidelines to such projects, background information about the history and landscape of Chicago community areas, and practical ideas for how to combine hardship index data with learning statistical and data science tools. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This study provides a glimpse into the professional learning, beliefs, and practices of high school teachers of Advanced Placement (AP) Statistics. Data are from a survey of 445 AP Statistics teachers in late 2018. Results indicate many AP Statistics teachers have taken several statistics courses and engage in professional development related to statistics sponsored by the College Board (summer institutes, exam readings, and online community). They generally do not engage with resources developed by the American Statistical Association and the statistics education community. While AP statistics teachers structure class time with student-student interaction and use student-centered activities, they generally do not use statistics-specific technology tools and rarely engage students with datasets larger than 100 cases or withmultiple variables. Teachers' beliefs about teaching statistics do not always reflect their teaching practices. Personal time to improve, time with students (especially those on a blocked semester schedule), structure of curriculum and exam schedule, and lack of access to technology often prevent teachers from making changes to their practices. Findings call for targeted efforts to reach high school statistics teachers, engage them more in the statistics education community, and encourage curriculum and instructional approaches that more closely align with recommendations and trends in college-level introductory statistics. [ABSTRACT FROM AUTHOR]

This article describes a suite of student-created Shiny apps for teaching statistics and a field test of their short-term effectiveness. To date, more than 50 Shiny apps and a growing collection of associated lesson plans, designed to enrich the teaching of both introductory and upper division statistics courses, have been developed. The apps are available for free use and their open source code can be adapted as desired. We report on the experimental testing of four of these Shiny apps to examine short-term learning outcomes in an introductory statistical concepts course. [ABSTRACT FROM AUTHOR]

In this article, we discuss our attempt to teach applied statistics techniques typically taught in advanced courses, such as clustering and principal component analysis, to a non-mathematical educated audience. Considering the negative attitude and inclination toward mathematical disciplines of our students we introduce them to our topics using four different games. The four games are all user-centric, score-based arcade experiences intended to be played under the supervision of an instructor. They are developed using the Shiny web-based application framework for R. In every activity students have to follow the instructions and to interact with plots to minimize a score with a statistical meaning. No other knowledge than elementary geometry and Euclidean distance is required to complete the tasks. Results froma student questionnaire give us some confidence that the experience has benefited students, not only in terms of their ability to understand and use the explained methods but also regarding their confidence and overall satisfaction with the course. This fact suggests that these or similar activities could greatly improve the diffusion of statistical thinking at different levels of education. [ABSTRACT FROM AUTHOR]

The Journal of Statistics and Data Science Education acknowledges and appreciates the contributions of the Associate Editors who played a crucial role in the success of the 2023 volume year. The list includes individuals from various universities and institutions, such as Bowling Green State University, University of Toronto, Grand Valley State University, and many others. Their expertise and dedication have greatly contributed to the journal's achievements. [Extracted from the article]

We provide a computational exercise suitable for early introduction in an undergraduate statistics or data science course that allows students to "play the whole game" of data science: performing both data collection and data analysis. While many teaching resources exist for data analysis, such resources are not as abundant for data collection given the inherent difficulty of the task. Our proposed exercise centers around student use of Google Calendar to collect data with the goal of answering the question "How do I spend my time?" On the one hand, the exercise involves answering a question with near universal appeal, but on the other hand, the data collection mechanism is not beyond the reach of a typical undergraduate student. A further benefit of the exercise is that it provides an opportunity for discussions on ethical questions and considerations that data providers and data analysts face in today's age of large-scale internet-based data collection. [ABSTRACT FROM AUTHOR]

A version control system records changes to a file or set of files over time so that changes can be tracked and specific versions of a file can be recalled later. As such, it is an essential element of a reproducible workflow that deserves due consideration among the learning objectives of statistics courses. This article describes experiences and implementation decisions of four contributing faculty who are teaching different courses at a variety of institutions. Each of these faculty has set version control as a learning objective and successfully integrated one such system(Git) into one or more statistics courses. The various approaches described in the article span different implementation strategies to suit student background, course type, software choices, and assessment practices. By presenting a wide range of approaches to teaching Git, the article aims to serve as a resource for statistics and data science instructors teaching courses at any level within an undergraduate or graduate curriculum. [ABSTRACT FROM AUTHOR]

Since the publishing of Nolan and Temple Lang's "Computing in the Statistics Curriculum" in 2010, the American Statistical Association issued new recommendations in the revised GAISE college report. To reflect modern practice and technologies, they emphasize giving students experience with multivariable thinking. Students develop multivariable thinking when they analyze real data in the context of investigating research questions of interest, which typically involve complex relationships between many variables. Proficiency in a statistical programming language facilitates the development of multivariable thinking by giving students tools to investigate complex data on their own. However, learning a programming language in an introductory course is difficult for many students. In this article, we recommend a set of computational skills for introductory courses, demonstrate them using R tidyverse, and describe a classroom activity to develop computational skills and multivariable thinking. We provide a tidyverse tutorial for introductory students, our course guide, and classroom activities. [ABSTRACT FROM AUTHOR]

In this article we present and discuss the importance and relevance of the inclusion of spatial data analysis as part of the undergraduate statistics curriculum. Given its usefulness and applicability across a range of disciplines, spatial data are a topic that should receive more emphasis within undergraduate statistics curricula. There exist many topical and intriguing spatial datasets from different areas of research that can bring fruitful discussions into the classroom including the environment, medicine, and political science. Spatial data can be incorporated at any level of study, from lower to upper division courses, as well as graduate-level courses in Mathematics and Statistics. This article will focus on its role in undergraduate statistics curricula, and present topics that can be included in an introductory course, a mathematical course in statistics, and an elective course in spatial data analysis. Supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]

Students learn many concepts in the introductory statistics course, but even our most successful students end up with rigid, ritualized knowledge that does not transfer easily to new situations. In this article we describe our attempt to apply theories and findings from learning science to the design of a statistics course that aims to help students build a coherent and interconnected representation of the domain. The resulting practicing connections approach provides students with repeated opportunities to practice connections between core concepts (especially the concepts of statistical model, distribution, and randomness), key representations (R programming language and computational techniques such as simulation and bootstrapping), and real-world situations statisticians face as they explore variation, model variation, and evaluate and compare statistical models. We provide a guided tour through our curriculum implemented in an interactive online textbook (CourseKata.org) and then provide some evidence that students who complete the course are able to transfer what they have learned to the learning of new statistical techniques. [ABSTRACT FROM AUTHOR]

Although statistical literacy has become a key competence in today's data-driven society, it is usually not a part of statistics education. To address this issue, we propose an innovative concept for a conference-like seminar on the topic of statistical literacy. This seminar draws attention to the relevance and importance of statistical literacy, and moreover, students are made aware of the process of science communication and are introduced to the peer review process for the assessment of scientific papers. In the summer term 2020, the seminar was conducted as a joint project by the University of Hamburg, the University of Muenster, and the Joachim Herz Foundation. In this article, we present the concept of the seminar and our experience with this concept in the summer term 2020. [ABSTRACT FROM AUTHOR]

Nolan and Temple Lang argued for the fundamental role of computing in the statistics curriculum. In the intervening decade, the statistics education community has acknowledged that computational skills are as important to statistics and data science practice as mathematics. There remains a notable gap, however, between our intentions and our actions. In this special issue of the Journal of Statistics and Data Science Education, we have assembled a collection of articles that (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking. We believe that it is critical for the community to redouble our efforts to embrace sophisticated computing in the statistics and data science curriculum. We hope that these articles provide useful guidance for the community to move these efforts forward. [ABSTRACT FROM AUTHOR]

The Journal of Statistics and Data Science Education special issue on "Computing in the Statistics and Data Science Curriculum "features a set of papers that provide amosaic of curricular innovations and approaches that embrace computing. As we reviewed the papers we felt that this collection would benefit from the perspective of the authors of the landmark "Computing in the Statistics Curricula" (TAS 2010) paper. We asked Deb and Duncan to take this opportunity to reflect on the landscape when they wrote the paper, to comment on the current situation, and to speculate on the future. [ABSTRACT FROM AUTHOR]