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  • What makes a good statistical question?

    Pip Arnold (New Zealand) & Chris Franklin (ASA K-12 Statistics Ambassador/ASA Fellow/UGA Emerita)
    Tuesday, April 27, 2021 - 4:00pm
    To event remaining 55 days
    In the April CAUSE/Journal of Statistics and Data Science Education webinar series, we discuss "What Makes a Good Statistical Question?" with Pip Arnold & Christine Franklin, the co-authors of a forthcoming paper in JSDSE (   The statistical problem-solving process is key to the statistics curriculum at the school level, post-secondary, and in statistical practice. The process has four main components: Formulate questions, collect data, analyze data, and interpret results. The Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education (GAISE) emphasizes the importance of distinguishing between a question that anticipates a deterministic answer and a question that anticipates an answer based on data that will vary, referred to as a statistical question. This paper expands upon the Pre-K-12 GAISE distinction of a statistical question by addressing and identifying the different types of statistical questions used across the four components of the statistical problem-solving process and the importance of interrogating these different statistical question types. Since the publication of the original Pre-K-12 GAISE document, research has helped to clarify the purposes of questioning at each component of the process, to clarify the language of questioning, and to develop criteria for answering the question, "What makes a good statistical question?" Pip Arnold is a statistics educator who also sometimes masquerades as a mathematics educator.  Her continuing interests include statistical questions, working to support with K-10 teachers in developing their statistical content knowledge and looking at ways to authentically integrate statistics across the curriculum. Pip has been developing a teacher's resource to support the teaching of statistics from K-10 for New Zealand teachers, based on the PPDAC statistical enquiry cycle that is the basis of statistical problem-solving in New Zealand. Christine (Chris) Franklin is the ASA K-12 Statistics Ambassador, an ASA Fellow, and UGA Emerita Statistics faculty. She is the co-author of two introductory statistics textbooks, chair for the ASA policy documents Pre-K-12 GAISE (2005) and Statistical Education of Teachers (2015), and co-chair for the recently published Pre-K-12 GAISE II. She is a former AP Statistics Chief Reader and a past Fulbright scholar to NZ, where she and Pip began having many conversations about the role of questioning in the statistical problem-solving process.
  • Data ingestation, data collection, and data analysis: key components in the statistics and data science analysis cycle

    Mine Dogucu (UC Irvine) & Albert Y. Kim (Smith College)
    Tuesday, March 23, 2021 - 4:00pm
    To event remaining 20 days
    In the March CAUSE/Journal of Statistics and Data Science Education webinar series we will discuss two related papers on data ingestation, data collection, and data analysis.   Mine Dogucu (UC Irvine) will discuss her paper "Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities" ( Albert Y. Kim (Smith College) will discuss his paper "'Playing the Whole Game': A Data Collection and Analysis Exercise With Google Calendar" ( Mine Dogucu is an Assistant Professor of Teaching in the Department of Statistics at University of California Irvine. Her work focuses on modern pedagogical approaches in the statistics curriculum, making data science education accessible, and undergraduate Bayesian education. She is the coauthor of the upcoming book Bayes Rules! An Introduction to Bayesian Modeling with R. She co-chairs the Undergraduate Statistics Project Competition and the Electronic Undergraduate Statistics Research Conference (USPROC+eUSR). She shares her thoughts about data science education on her Data Pedagogy blog. Albert Kim is an Assistant Professor of Statistical & Data Sciences at Smith College as well as a Visiting Scholar at the ForestGEO network's Smithsonian Conservation Biology Institute (SCBI) large forest dynamics plot. His research centers on forest ecology, in particular modeling the impact of climate change on the growth of trees as well as ecological forecasting. He is a co-author of "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" (see
  • Bayesian Methods and the Statistics and Data Science Curriculum

    Jingchen (Monika) Hu (Vassar College), Kevin Ross (Cal Poly - San Luis Obispo), & Colin Rundel (University of Edinburgh/Duke)
    Tuesday, February 23, 2021 - 4:00pm
    The Journal of Statistics and Data Science Education recently published a cluster of papers on Bayesian methods ( The Bayesian cluster includes a presentation of how and why Bayesian ideas should be added to the curriculum; guidance on how to structure a semester-long Bayesian course for undergraduates; a discussion of the ever-evolving nature of Bayesian computing; a book review; and a panel interview of several Bayesian educators. For the February CAUSE/JSDSE webinar series, we’ve invited several authors of these provocative and informative articles to describe their work and its implications for statistics and data science education. Jingchen (Monika) Hu is an Assistant Professor of Mathematics and Statistics at Vassar College. She teaches an undergraduate-level Bayesian Statistics course at Vassar, which is shared online across several liberal arts colleges. Her research focuses on dealing with data privacy issues by releasing synthetic data. Kevin Ross is an Associate Professor of Statistics at Cal Poly San Luis Obispo. His research interests include probability, stochastic processes and applications as well as probability and statistics education. Colin Rundel is a lecturer at the University of Edinburgh and an assistant professor of the practice in Statistical Science at Duke University. His research interests include applied spatial statistics with an emphasis on Bayesian statistics and computational methods.
  • Teacher Education Curriculum Materials that Develop Statistical Knowledge for Teaching

    Stephanie Casey, Andrew Ross (Eastern Michigan University)
    Tuesday, February 9, 2021 - 2:00pm
    Statistics is more important than ever in today's data-driven world. This is reflected in the increased level of statistics understanding expected of K-12 students according to the CCSS-M and state-level standards. To develop middle and high school teachers' statistical knowledge for teaching, the MODULE(S^2) project has created curriculum materials for use in introductory statistics course(s) that preservice secondary teachers take. The materials develop preservice teachers’  subject matter and pedagogical content knowledge for teaching statistics as well as their equity literacy. In this webinar, we will provide an introduction to these materials including examples of statistical tasks and classroom videos from the materials. Alignment of these materials with ASA’s GAISE, ASA’s Statistical Education of Teachers report, and the Association of Mathematics Teacher Educator's Standards for Preparing Teachers of Mathematics will be highlighted. Also, we are recruiting faculty to be piloters for the materials. To find sample materials, visit, and to indicate you are interested in piloting, please fill out the form at
  • Computing in the Statistics and Data Science Curriculum

    Mine Çetinkaya-Rundel (University of Edinburgh/RStudio) & Alex Reinhart (Carnegie Mellon University)
    Tuesday, January 26, 2021 - 4:00pm
    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 a mosaic of curricular innovations and approaches that embrace computing. The collected papers (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking. In this webinar, we've invited two authors of papers in the special issue to talk about their work and to answer questions originally posed by Nolan and Temple Lang in their 2010 TAS paper "Computing in the Statistics Curriculum": When they graduate, what ought our students be able to do computationally, and are we preparing them adequately in this regard? Do we provide students the essential skills needed to engage in statistical problem solving and keep abreast of new technologies as they evolve? Do our students build the confidence needed to overcome computational challenges to, for example, reliably design and run a synthetic experiment or carry out a comprehensive data analysis? Overall, are we doing a good job preparing students who are ready to engage in and succeed at statistical inquiry?
  • Students Building Shiny Apps: 
An Update on the 
BOAST Project

    Neil Hatfield, Leah Hunt, Ethan Wright, Gonghao Liu, Xigang Zhang, & Zeyuan (Primo) Wang (Penn State University)
    Tuesday, December 8, 2020 - 2:00pm
    For the past four years, teams of Penn State statistics and data science undergraduates have spent the summer and fall developing apps for teaching statistical concepts. Their work has culminated in over 60 apps as part of the Book of Apps for Statistics Teaching (BOAST). This webinar will share some details of the project and give some of the students the opportunity to highlight some of the newest apps they have developed. Visit:
  • Challenges in and Educational Strategies for Teaching Causal Inference in the Health Sciences

    Douglas Landsittel (University of Pittsburgh)
    Thursday, November 12, 2020 - 2:00pm
    Many areas of clinical research, such as comparative effectiveness research and patient-centered outcomes research, strongly depend on making causal inferences from observational data. Further, these topic areas also utilize pragmatic trials and quasi-experimental designs, where consistent estimation of causal effects is also more challenging than traditional randomized controlled trials, and/or involves distinct approaches for intention-to-treat versus as-treated or per-protocol effects. While substantial literature exists on associated designs and analysis strategy, the corresponding methods are complex and not always taught in formal training, even within graduate statistics or biostatistics programs. Therefore, a critical need exists for accessible educational resources and the expansion of relevant courses and training programs. Regarding that goal, however, significant debate exists on whether these advanced methods should even be taught at all to non-statisticians, and/or researchers with more limited statistical training (e.g. a fundamental course and some background in regression). This talk proposes some possible perspectives to effectively address these concerns, while still avoiding the result of "knowing enough to be dangerous". The presenter has some related links at This work was supported by AHRQ grant R25HS023185, PCORI contract R-IMC-1306-03827, and supplemental funding from the NIH/NLM grant 5 T15 LM007059-32.
  • Exploring and Utilizing the TSHS Resources Portal

    Amy Nowacki (Cleveland Clinic) & Carol Bigelow (University of Massachusetts)
    Wednesday, July 29, 2020 - 1:00pm
    The TSHS Resources Portal ( contains datasets from 13 real health sciences research studies. Each dataset is accompanied by a study description and a data dictionary. Most are linked to a published paper as well. These datasets, plus some extra teaching tools, are peer reviewed and ready for use with your learners. In this webinar, Amy and Carol will walk through what is available and how to get the most out of this resource.
  • Causal Inference: Why We Should and How We Can Teach it in Introductory Courses

    Karsten Lübke (FOM University)
    Tuesday, June 9, 2020 - 2:00pm
    We are living in a world full of multivariate observational data. Qualitative assumptions about the data generating process, operationalised in simple directed acyclic graph can help students to understand multivariate phenomena like Simpson's or Berkson's paradox, confounding and bias. By teaching causal inference the introductory course can overcome the mantra "correlation does not imply causation". The webinar discusses some motivation as well as teaching ideas and the integration in the curriculum.
  • Out of the Classroom and into the 'Real' World: Learning Statistics by Doing Statistics with 'The Islands'

    Ann Brearley, PhD (University of Minnesota)
    Thursday, April 23, 2020 - 2:00pm
    Over the past 10 years we have adopted a variety of new teaching methods to make both our in-person and our online introductory biostatistics courses more active, relevant and effective. These include the flipped classroom approach, active learning, collaborative answer keys, and group projects using “The Islands”. The virtual world of The Islands, created by Michael Bulmer at the University of Queensland, allows students to actually do research (and statistics) from start to finish by designing, executing, analyzing and reporting the results of a “real” study on virtual people. We have collaborated with Dr. Bulmer to add features to The Islands (such as clinics and hospitals) to facilitate health-related research studies, both experimental and observational. Carrying out an Island study provides students with sometimes painful but nevertheless invaluable experience in many aspects of research, including study design, data collection, teamwork, data analysis, and communicating research results to others. This webinar will describe The Islands and how we use them for student projects and will discuss the benefits and challenges of these projects, both for students and for instructors. Webinar Recap: