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Permutation tests are a powerful and flexible approach to inference via resampling. As computational methods become more ubiquitous in the statistics curriculum, use of permutation tests has become more tractable. At the heart of the permutation approach is the exchangeability assumption, which determines the appropriate null sampling distribution. We explore the exchangeability assumption in the context of permutation tests for multiple linear regression models. Various permutation schemes for the multiple linear regression setting have been previously proposed and assessed in the literature. As has been demonstrated previously, in most settings, the choice of how to permute a multiple linear regression model does not materially change inferential conclusions. Regardless, we believe that (1) understanding exchangeability in the multiple linear regression setting and also (2) how it relates to the null hypothesis of interest is valuable. We also briefly explore model settings beyond multiple linear regression (e.g., settings where clustering or hierarchical relationships exist) as a motivation for the benefit and flexibility of permutation tests. We close with pedagogical recommendations for instructors who want to bring multiple linear regression permutation inference into their classroom as a way to deepen student understanding of resampling-based inference.

In 2022, the Journal of Statistics and Data Science Education (JSDSE) instituted augmented requirements for authors to post deidentified data and code underlying their papers. These changes were prompted by an increased focus on reproducibility and open science (NASEM 2019). A recent review of data availability practices noted that "such policies help increase the reproducibility of the published literature, as well as make a larger body of data available for reuse and re-analysis" (PLOS ONE, 2024). JSDSE values accessibility as it endeavors to share knowledge that can improve educational approaches to teaching statistics and data science. Because institution, environment, and students differ across readers of the journal, it is especially important to facilitate the transfer of a journal article's findings to new contexts. This process may require digging into more of the details, including the deidentified data and code. Our goal is to provide our readers and authors with a review of why the requirements for code and data sharing were instituted, summarize ongoing trends and developments in open science, discuss options for data and code sharing, and share advice for authors.

Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order to observe how students reason with text data in scenarios designed to elicit certain aspects of the domain, we employed a task-based interview method using a structured protocol with six pairs of undergraduate students. Our goal was to shed light on students' understanding of text as data using a motivating task to classify headlines as "clickbait" or "news". Three types of features (function, content, and form) surfaced, the majority from the first scenario. Our analysis of the interviews indicates that this sequence of activities engaged the participants in thinking at both the human-perception level and the computer-extraction level and conceptualizing connections between them.

A substantial fraction of students who complete their college education at a public university in the United States begin their journey at one of the 935 public two-year colleges. While the number of four-year colleges offering bachelor's degrees in data science continues to increase, data science instruction at many two-year colleges lags behind. A major impediment is the relative paucity of introductory data science courses that serve multiple student audiences and can easily transfer. In addition, the lack of pre-defined transfer pathways (or articulation agreements) for data science creates a growing disconnect that leaves students who want to study data science at a disadvantage. We describe opportunities and barriers to data science transfer pathways. Five points of curricular friction merit attention: 1) a first course in data science, 2) a second course in data science, 3) a course in scientific computing, data science workflow, and/or reproducible computing, 4) lab sciences, and 5) navigating communication, ethics, and application domain requirements in the context of general education and liberal arts course mappings. We catalog existing transfer pathways, efforts to align curricula across institutions, obstacles to overcome with minimally-disruptive solutions, and approaches to foster these pathways. Improvements in these areas are critically important to ensure that a broad and diverse set of students are able to engage and succeed in undergraduate data science programs.

Many data science students and practitioners don't see the value in making time to learn and adopt good coding practices as long as the code "works". However, code standards are an important part of modern data science practice, and they play an essential role in the development of data acumen. Good coding practices lead to more reliable code and save more time than they cost, making them important even for beginners. We believe that principled coding is vital for quality data science practice. To effectively instill these practices within academic programs, instructors and programs need to begin establishing these practices early, to reinforce them often, and to hold themselves to a higher standard while guiding students. We describe key aspects of good coding practices for data science, illustrating with examples in R and in Python, though similar standards are applicable to other software environments. Practical coding guidelines are organized into a top ten list.

The world is full of text data, yet text analytics has not traditionally played a large part in statistics education. We consider four different ways to provide students with opportunities to explore whether email messages are unwanted correspondence (spam). Text from subject lines are used to identify features that can be used in classification. The approaches include use of a Model Eliciting Activity, exploration with CODAP, modeling with a specially designed Shiny app, and coding more sophisticated analyses using R. The approaches vary in their use of technology and code but all share the common goal of using data to make better decisions and assessment of the accuracy of those decisions., Comment: in press, CHANCE

Computing makes up a large and growing component of data science and statistics courses. Many of those courses, especially when taught by faculty who are statisticians by training, teach R as the programming language. A number of instructors have opted to build much of their teaching around use of the tidyverse. The tidyverse, in the words of its developers, "is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next". These shared principles have led to the widespread adoption of the tidyverse ecosystem. A large part of this usage is because the tidyverse tools have been intentionally designed to ease the learning process and make it easier for users to learn new functions as they engage with additional pieces of the larger ecosystem. Moreover, the functionality offered by the packages within the tidyverse spans the entire data science cycle, which includes data import, visualisation, wrangling, modeling, and communication. We believe the tidyverse provides an effective and efficient pathway for undergraduate students at all levels and majors to gain computational skills and thinking needed throughout the data science cycle. In this paper, we introduce the tidyverse from an educator's perspective. We provide a brief introduction to the tidyverse, demonstrate how foundational statistics and data science tasks are accomplished with the tidyverse, and discuss the strengths of the tidyverse, particularly in the context of teaching and learning.

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their technical and non-technical data science skills, the project promoted a team-based approach to data science, adopting several processes and tools intended to facilitate this collaboration. Evidence from the project evaluation, including participant survey and interview data, is presented to document the degree to which the project was successful in engaging students in team-based data science, and how the project changed the students' perceptions of their technical and non-technical skills. We also examine opportunities for improvement and offer insight to other data science educators who may want to implement a similar team-based approach to data science projects at their own institutions.

Nolan and Temple Lang (2010) 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 papers 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 papers provide useful guidance for the community to move these efforts forward., Comment: In press, Journal of Statistics and Data Science Education

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 and data science courses. This paper describes experiences and implementation decisions of four contributing faculty who are teaching different courses at a variety of institutions. Each of these faculty have 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 paper 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 paper aims to serve as a resource for statistics and data science instructors teaching courses at any level within an undergraduate or graduate curriculum., Comment: In press, Journal of Statistics and Data Science Education

Donoho's JCGS (in press) paper is a spirited call to action for statisticians, who he points out are losing ground in the field of data science by refusing to accept that data science is its own domain. (Or, at least, a domain that is becoming distinctly defined.) He calls on writings by John Tukey, Bill Cleveland, and Leo Breiman, among others, to remind us that statisticians have been dealing with data science for years, and encourages acceptance of the direction of the field while also ensuring that statistics is tightly integrated. As faculty at baccalaureate institutions (where the growth of undergraduate statistics programs has been dramatic), we are keen to ensure statistics has a place in data science and data science education. In his paper, Donoho is primarily focused on graduate education. At our undergraduate institutions, we are considering many of the same questions., Comment: in press response to Donoho paper in Journal of Computational Graphics and Statistics

Since the 2005 American Statistical Association's (ASA) endorsement of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report, changes in the statistics field and statistics education have had a major impact on the teaching and learning of statistics. We now live in a world where "Statistics - the science of learning from data - is the fastest-growing science, technology, engineering, and math (STEM) undergraduate degree in the United States," according to the ASA, and where many jobs demand an understanding of how to explore and make sense of data. In light of these new reports and other changes and demands on the discipline, a group of volunteers revised the 2005 GAISE College Report. The updated report was endorsed by the Board of Directors of the American Statistical Association in July 2016. To help shed additional light on the revision process and subsequent changes in the report, we review the report and share insights into the committee's thoughts and assumptions., Comment: in press, CHANCE

Most analyses of randomised trials with incomplete outcomes make untestable assumptions and should therefore be subjected to sensitivity analyses. However, methods for sensitivity analyses are not widely used. We propose a mean score approach for exploring global sensitivity to departures from missing at random or other assumptions about incomplete outcome data in a randomised trial. We assume a single outcome analysed under a generalised linear model. One or more sensitivity parameters, specified by the user, measure the degree of departure from missing at random in a pattern mixture model. Advantages of our method are that its sensitivity parameters are relatively easy to interpret and so can be elicited from subject matter experts; it is fast and non-stochastic; and its point estimate, standard error and confidence interval agree perfectly with standard methods when particular values of the sensitivity parameters make those standard methods appropriate. We illustrate the method using data from a mental health trial., Comment: pre-publication (author version) in press, Statistica Sinica

Confidence intervals provide a way to determine plausible values for a population parameter. They are omnipresent in research articles involving statistical analyses. Appropriately, a key statistical literacy learning objective is the ability to interpret and understand confidence intervals in a wide range of settings. As instructors, we devote a considerable amount of time and effort to ensure that students master this topic in introductory courses and beyond. Yet, studies continue to find that confidence intervals are commonly misinterpreted and that even experts have trouble calibrating their individual confidence levels. In this article, we present a ten-minute trivia game-based activity that addresses these misconceptions by exposing students to confidence intervals from a personal perspective. We describe how the activity can be integrated into a statistics course as a one-time activity or with repetition at intervals throughout a course, discuss results of using the activity in class, and present possible extensions., Comment: 18 pages; in press at the American Statistician

One learning goal of the introductory statistics course is to develop the ability to make sense of research findings in published papers. The Atlantic magazine regularly publishes a feature called "Study of Studies" that summarizes multiple articles published in a particular domain. We describe a classroom activity to develop this capacity using the "Study of Studies." In this activity, students read capsule summaries of twelve research papers related to restaurants and dining that was published in April 2015. The selected papers report on topics such as how seating arrangement, server posture, plate color and size, and the use of background music relate to revenue, ambiance, and perceived food quality. The students are assigned one of the twelve papers to read and critique as part of a small group. Their group critiques are shared with the class and the instructor. A pilot study was conducted during the 2015-2016 academic year at Amherst College. Students noted that key details were not included in the published summary. They were generally skeptical of the published conclusions. The students often provided additional summarization of information from the journal articles that better describe the results. By independently assessing and comparing the original study conclusions with the capsule summary in the "Study of Studies," students can practice developing judgment and assessing the validity of statistical results., Comment: in press, CHANCE

Many have argued that statistics students need additional facility to express statistical computations. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically. In an era of increasingly big data, it is imperative that students develop data-related capacities, beginning with the introductory course. We believe that the integration of these precursors to data science into our curricula-early and often-will help statisticians be part of the dialogue regarding "Big Data" and "Big Questions".

A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to utilize databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this paper is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of undergraduates with data and data science.

Nolan and Temple Lang argue that "the ability to express statistical computations is an essential skill." A key related capacity is the ability to conduct and present data analysis in a way that another person can understand and replicate. The copy-and-paste workflow that is an artifact of antiquated user-interface design makes reproducibility of statistical analysis more difficult, especially as data become increasingly complex and statistical methods become increasingly sophisticated. R Markdown is a new technology that makes creating fully-reproducible statistical analysis simple and painless. It provides a solution suitable not only for cutting edge research, but also for use in an introductory statistics course. We present evidence that R Markdown can be used effectively in introductory statistics courses, and discuss its role in the rapidly-changing world of statistical computation., Comment: 20 pages, plus a 10 page appendix

Statistics students need to develop the capacity to make sense of the staggering amount of information collected in our increasingly data-centered world. Data science is an important part of modern statistics, but our introductory and second statistics courses often neglect this fact. This paper discusses ways to provide a practical foundation for students to learn to "compute with data" as defined by Nolan and Temple Lang (2010), as well as develop "data habits of mind" (Finzer, 2013). We describe how introductory and second courses can integrate two key precursors to data science: the use of reproducible analysis tools and access to large databases. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically in the era of big data.

An establishment's average wage, computed from administrative wage data, has been found to be related to occupational wages. These occupational wages are a primary outcome variable for the Bureau of Labor Statistics Occupational Employment Statistics survey. Motivated by the fact that nonresponse in this survey is associated with average wage even after accounting for other establishment characteristics, we propose a method that uses the administrative data for imputing missing occupational wage values due to nonresponse. This imputation is complicated by the structure of the data. Since occupational wage data is collected in the form of counts of employees in predefined wage ranges for each occupation, weighting approaches to deal with nonresponse do not adequately adjust the estimates for certain domains of estimation. To preserve the current data structure, we propose a method to impute each missing establishment's wage interval count data as an ordered multinomial random variable using a separate survival model for each occupation. Each model incorporates known auxiliary information for each establishment associated with the distribution of the occupational wage data, including geographic and industry characteristics. This flexible model allows the baseline hazard to vary by occupation while allowing predictors to adjust the probabilities of an employee's salary falling within the specified ranges. An empirical study and simulation results suggest that the method imputes missing OES wages that are associated with the average wage of the establishment in a way that more closely resembles the observed association., Comment: Published in at http://dx.doi.org/10.1214/14-AOAS714 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)