Combating Anti-Statistical Thinking Using Simulation-Based Methods Throughout the Undergraduate Curriculum

Tintle, Nathan L.; Chance, Beth; Cobb, George W.; Roy, Soma; Swanson, Todd; VanderStoep, Jill

doi:10.1080/00031305.2015.1081619

Cited by 48 publications

(49 citation statements)

References 24 publications

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“…Resampling approaches have become increasingly important in statistical education (Tintle et al, 2015;Hesterberg, 2015). The mosaic package provides simplified functionality to support teaching inference based on randomization tests and bootstrap methods.…”

Section: Randomization and Resamplingmentioning

confidence: 99%

The mosaic Package: Helping Students to Think with Data Using R

Pruim¹,

Kaplan²,

Horton³

2017

The R Journal

114

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The mosaic package provides a simplified and systematic introduction to the core functionality related to descriptive statistics, visualization, modeling, and simulation-based inference required in first and second courses in statistics. This introduction to the package describes some of the guiding principles behind the design of the package and provides illustrative examples of several of the most important functions it implements. These can be combined to help students "think with data" using R in their early course work, starting with simple, yet powerful, declarative commands. MotivationIn order to make sense of rich data that is increasingly available, students need computational tools and facility for data management, exploratory analysis, visualization, and modeling (e.g., Nolan and Lang (2010);; Horton and Hardin (2015); Ridgway (2016)). To extract useful information from the complex systems that generate this rich data, students should learn to "think with data" (in the phrase coined by Diane Lambert of Google): using previous results from data to drive statistical investigations and to inform the choice of analysis and presentation possibilities.Yet many students enter statistics courses with little or no computational experience. Software such as R that encompass the tools for thinking with data are sometimes regarded as off-putting and inaccessible to students. With the mosaic package, we have sought to remove unnecessary difficulty in the use of R by students. Our students have demonstrated that it is feasible to integrate computing into our curricula early and often, in a way that provides students with success, confidence, and room to grow.The mosaic (Pruim et al., 2016b) package reflects attempts by each of the authors to make the power of R accessible and rewarding to students, especially in the context of undergraduate statistics courses, and, in one case, also in calculus. One of the guiding principles behind the development of the mosaic package has been "Less volume, more creativity." By "less volume," we mean reducing the cognitive load involved in using R. By "more creativity" we mean two things. First, we want to provide access to R tools in a way that fosters choices and decision making by students. Such decisions might be about modes of analysis or visualization or about the variables and covariates to consider when exploring relationships with data. Second, we want to encourage students to creatively engage with statistical inference methods via simulation techniques such as randomization and resampling with data analysis and presentation.Our route to "less volume" is to provide a set of three command templates -formulas, functions, and extractors -that standardize usage across many tasks and that highlight the connections between graphical summaries, numerical summaries, models, and inference. The templates themselves are designed to be consistent and concise. Consistency enables a student to generalize from a specific task to a wide set of possibilities. Conciseness means avoiding un...

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Section: Randomization and Resamplingmentioning

confidence: 99%

The mosaic Package: Helping Students to Think with Data Using R

Pruim¹,

Kaplan²,

Horton³

2017

The R Journal

114

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“…Los métodos de computación intensiva mediante simulación para la realización de inferencias han ido ganando presencia en las propuestas de enseñanza de la Estadística en el nivel introductorio y han comenzado a incluirse parcial o totalmente, por ejemplo, en los manuales y libros de texto (véase p.e. Diez, Barr y Cetinkaya-Rundel, 2014;Lock et al, 2012;Tintle, Chance, Cobb, Rossman et al, 2015). Baste este último apunte como reflexión final sobre la necesidad de mantener una mirada atenta y de futuro sobre los desarrollos tanto disciplinares como metodológicos en un área docente actualmente en fuerte evolución.…”

Section: Consideraciones Finalesunclassified

Directrices y recursos para la innovación en la enseñanza de la Estadística en la universidad: una revisión documental

Blanco

2018

REDU

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<p>La Estadística es una disciplina presente en numerosos planes de estudios universitarios de titulaciones muy diversas, en las áreas de Ciencias Naturales, Ciencias Biosanitarias, Ingeniería o Ciencias Sociales. Debido a su papel en la formación general del alumnado y a la fuerte evolución de la disciplina en los últimos años, la enseñanza de la Estadística en la universidad plantea hoy retos específicos. En este contexto, este trabajo adopta la perspectiva de la comunidad estadística norteamericana con el objetivo de ofrecer un posible marco para la innovación en la enseñanza de la Estadística en el nivel introductorio y para alumnado con otro ámbito de especialización (p.e. Ciencias Sociales). En primer lugar se realiza una revisión documental centrada en las orientaciones y pautas ofrecidas por la American Statistical Association desde fines del siglo pasado hasta nuestros días. Se trata así de mostrar una visión actualizada de los parámetros que, desde este enfoque, deberían definir la enseñanza de la Estadística en la actualidad. En segundo lugar se presenta un inventario de recursos útiles para el desarrollo de propuestas docentes alineadas con tales orientaciones y, en general, para la actualización y la innovación de los procesos de enseñanza-aprendizaje de la Estadística. Aunque el foco del trabajo se pone en los cursos de introducción a la Estadística genéricamente considerados, algunas ejemplificaciones se refieren más específicamente al ámbito de las Ciencias Sociales, del Comportamiento y de la Educación. El trabajo se cierra con algunos apuntes adicionales sobre nuevas vías de futuro para la innovación curricular.</p>

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“…Journal editors have suggested the drastic step of banning null hypothesis significance testing (Trafimow and Marks 2015), and the statistics community has responded with both enthusiasm (McShane and Gal 2016;White and Gorard 2017) and skepticism (Carlin 2016;Wasserstein and Lazar 2016). Simulationbased inference has become a popular alternative in the statistics education community to traditional inferential statistics (Tintle et al 2015;Maurer and Lock 2016;Hildreth, Robison-Cox, and Schmidt 2018;Case, Battles, and Jacobbe 2019), and may help address persistent misconceptions about inference. These changes to the curriculum have been incorporated in many "Stat 101" courses, however, it is unclear what, if any, changes have been made in discipline-specific statistics courses to address both recent controversies and advances in statistics.…”

Section: Introductionmentioning

confidence: 99%

The State of Statistics Education Research in Client Disciplines: Themes and Trends Across the University

Schwab-McCoy

2019

Journal of Statistics Education

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The reform movement in statistics education has led to a revitalization of the undergraduate introductory statistics course. However, many students satisfy their degree requirements by taking statistics courses in "client departments" such as business, the social sciences, and the lab sciences, typically taught by nonstatisticians. This article presents the findings of a metasynthesis of the existing literature on teaching statistics in these client disciplines to learn (1) what is currently being taught and how, and (2) the most important challenges for statistics teachers in other departments. Articles were reviewed using qualitative axial coding and quantitative text analysis to identify common research themes and ideas in the literature for each discipline. Research themes, attitudes toward statistics instruction, and pedagogical techniques were found to vary from discipline to discipline. Collaboration with instructors in other disciplines may be a welcome step toward improving statistics instruction across the university.

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Combating Anti-Statistical Thinking Using Simulation-Based Methods Throughout the Undergraduate Curriculum

Cited by 48 publications

References 24 publications

The mosaic Package: Helping Students to Think with Data Using R

The mosaic Package: Helping Students to Think with Data Using R

Directrices y recursos para la innovación en la enseñanza de la Estadística en la universidad: una revisión documental

The State of Statistics Education Research in Client Disciplines: Themes and Trends Across the University

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