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As the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re-conceptualizing how we teach undergraduate statistics and data science courses for majors and non-majors alike. In this paper, we focus on three crucial components for this re-conceptualization: Developing research questions, professional ethics, and team collaborations. We share vignettes from two teams of undergraduate statistics or data science majors at two different stages of their development (novice and expert) while they worked on a DataFest data challenge. These vignettes shed light on opportunities for re-conceptualizing introductory courses to give more attention to issues of the process of developing focused research questions when given a complex data set, professional ethics and bias, and how to collaborate effectively with others. We provide some implications for teaching and learning as well as an example activity for educators to use in their courses.

Using simulation approaches when conducting randomization tests for comparing two groups in the context of experimental studies has been promoted as a beneficial approach for supporting student learning of statistical inference. Many researchers have suggested that the data production process in simulations for the randomization test intuitively connects to the random assignment used in the original study design, thus supporting students' understanding of the logic of inference. Yet, there is little empirical research on how students initially think about the concepts and processes underlying the randomization test as they engage in constructing and using probability models to solve a problem. This work makes a contribution by deepening our understanding of students' reasoning about randomization tests by focusing on a group of three students as they create and use a TinkerPlots model to simulate data and use this data to make a statistical inference. This work adopts a narrative lens through which to view these students' reasoning and modeling activity. We compare and contrast the narratives we constructed for these students along with a narrative we constructed for a statistician. We discuss possible implications for teaching randomization tests for comparing two groups using a modeling and simulation approach.

Initial research has shown that simulating data from models created with computer software may enhance students' understanding of concepts in introductory statistics; yet, there is little research investigating students' development of statistical models. The research presented here examines small groups of students as they develop a model for a situation where a music teacher plays ten notes for a student who tries to guess each of the notes correctly. As students constructed their models and described their thinking, their descriptions were narrative in nature, focusing on the story of notes played and guessed. In this context, their focus on narrative appeared to support the development of productive statistical models. In addition, when students investigated pre-built TinkerPlots models, they preferred models that they perceived as more communicative or narrative in nature. These results have important pedagogical implications in terms of designing modeling curriculum.

Teaching introductory statistics using curricula focused on modeling and simulation is becoming increasingly common in introductory statistics courses and touted as a more beneficial approach for fostering students' statistical thinking. Yet, surprisingly little research has been conducted to study the impact of modeling and simulation curricula on student thinking, nor is there much research on how students make sense of the computer models they construct. The work presented here utilizes a framework developed by Biehler, Frischemeier, and Podworny (2015) for comparing two groups problems via a modeling and simulation approach using TinkerPlots™. Our work makes a contribution to the field by delving deeper into student reasoning as students create TinkerPlots™ models to solve a comparing two groups problem.

Statistics education reform efforts emphasize the importance of informal inference in the learning of statistics. Research suggests statistics teachers experience similar difficulties understanding statistical inference concepts as students and how teacher knowledge can impact student learning. This study investigates how teachers reinvented an informal hypothesis test for categorical data through the framework of guided reinvention. We describe how notions of variability help bridge the development from informal to formal understandings of empirical sampling distributions and procedures for constructing statistics and critical values for conducting hypothesis tests. A product of this paper is a hypothetical learning trajectory that statistics educators could utilize as both a framework for research and as an instructional tool to improve the teaching of hypothesis testing.

This research investigates what students' use of statistical language can tell us about their conceptions of distribution and sampling in relation to informal inference. Prior research documents students' challenges in understanding ideas of distribution and sampling as tools for making informal statistical inferences. We know that these ideas are complex and difficult for students, but little is known about the ways in which students' language mediates their statistical problem-solving activities within the realm of distribution, sampling, and informal inference. This study uses examples from semi-structured interviews with eleven undergraduate students from two universities. Interview tasks focused on (1) distinctions between distributions of populations, samples, and sample statistics; (2) properties of sampling distributions; and (3) how to use sampling distributions to make informal inferences. Analysis focused on students' use of metonymy (i.e., the substitution of the name of an attribute or adjunct for that of the thing or idea meant). We observed two particular metonymies. The first was a paradigmatic metonymy in which students applied the properties of the normal distribution to all distributions. Second, we observed a proper metonymy in which students talked about sampling distributions as compilations of many samples. The potential impact of these metonymies on students' ability to solve problems and the implications for teaching are discussed.

The "law of large numbers" indicates that as sample size increases, sample statistics become less variable and more closely estimate their corresponding population parameters. Different research studies investigating how people consider sample size when evaluating the reliability of a sample statistic have found a wide range of responses, from no consideration of sample size to over consideration of sample size. This paper provides a qualitative meta-analysis of studies that have used what we dub the "Hospital Task" for investigating peoples' thinking about the role of sample size in parameter estimation. This paper aims to investigate what the Hospital Task can tell us about how people make decisions under uncertainty and the usefulness of the task for developing models of students' statistical reasoning. To achieve these goals, we review the original task, synthesize results of other studies which have used some version of this task, provide a critique of the different versions of the task, discuss implications of the task for research, and provide insights and viewpoints from a small group of statisticians. The paper concludes with implications for further research.

They also observed that spending time having teachers move from data visualizations to data tables is also challenging, but may be a fruitful approach supporting better understanding of the relationships between data and visualizations. Erickson and Engel [[5]] utilize fairly traditional data; however, they explore these data through nontraditional forms of data visualization. The papers in this issue are an important starting point for considering how statistics and data science educators can begin the hard work of preparing students for a more complex society, one driven by data (real and fake) and computing technologies. Traditional statistics education has focused on data from random samples and has capitalized on knowledge about a sample to understand an unknown population. [Extracted from the article]

Sampling tasks and sampling distributions provide a fertile realm for investigating students' conceptions of variability. A project-designed teaching episode on samples and sampling distributions was team-taught in 6 research classrooms (2 middle school and 4 high school) by the investigators and regular classroom mathematics teachers. Data sources included survey data collected in 6 research classes and 4 comparison classes both before and after the teaching episode, and semistructured task-based interviews conducted with students from the research classes. Student responses and reasoning on sampling tasks led to the development of a conceptual lattice that characterizes types of student reasoning about sampling distributions. The lattice may serve as a useful conceptual tool for researchers and as a potential instructional tool for teachers of statistics. Results suggest that teachers need to focus explicitly on multiple aspects of distributions, especially variability, to enhance students' reasoning about sampling distributions. (Contains 17 tables, 11 figures and 2 footnotes.)

Research investigating graduate teaching assistants' (TAs') knowledge of fundamental statistics concepts is sparse at best; yet at many universities, TAs play a substantial role in the teaching of undergraduate statistics courses. This paper provides a framework for characterizing TAs' content knowledge in a sampling context and endeavors to raise new questions about TAs' content knowledge and its potential impact on the teaching of undergraduate statistics. The participants in this study were sixty-eight TAs from 18 universities across the United States. These TAs demonstrated considerable knowledge of theoretical probability distributions. However, they experienced tensions when attempting to quantify expected statistical variability in an empirical sampling situation and had difficulty explaining conceptual ideas of variability. (Contains 4 figures and 10 tables.)

Compared to more traditional classroom activities where students collect and analyze their own classroom data, publicly available data sets are complex, larger in scope and often involve dimensions that obscure the lines between finding patterns in the data and recognizing sources of variability. Exploring variability during data preparation: A way to connect data, chance, and context when working with complex public datasets. For managing existing, messy, and multivariate data sets, Kazak et al. modify the PPDAC cycle - typically used to describe projects in which students collect their own data - by emphasizing the data exploration phase in the beginning. [Extracted from the article]

Teaching introductory statistics using curricula focused on modeling and simulation is becoming increasingly common in introductory statistics courses and touted as a more beneficial approach for fostering students’ statistical thinking. Yet, surprisingly little research has been conducted to study the impact of modeling and simulation curricula on student thinking, nor is there much research on how students make sense of the computer models they construct. The work presented here utilizes a framework developed by Biehler, Frischemeier, and Podworny (2015) for comparing two groups problems via a modeling and simulation approach using TinkerPlotsTM. Our work makes a contribution to the field by delving deeper into student reasoning as students create TinkerPlotsTM models to solve a comparing two groups problem. [ABSTRACT FROM AUTHOR]

A review of the article "Graduate Teaching Assistants' Statistical Content Knowledge of Sampling," by Jennifer Noll, which appeared in the journal "Statistics Education Research Journal" on 2011, is presented.