In the September CAUSE/Journal of Statistics and Data Science Education webinar series, we talk with Julia Sharp, Emily Griffith, and Megan Higgs, the co-authors of a forthcoming JSDSE paper entitled “Setting the stage: Statistical collaboration videos for training the next generation of applied statisticians” (https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.tandf…);reserved=0).
Collaborative work is inherent to being a statistician or data scientist, yet opportunities for training and exposure to real-world scenarios are often only a small part of a student’s academic program. Resources to facilitate effective and meaningful instruction in communication and collaboration are limited, particularly when compared to the abundant resources available to support traditional statistical training in theory and methods. This paper helps fill the need for resources by providing ten modern, freely-available videos of mock collaborative interactions, with supporting discussion questions, scripts, and other resources. Videos are particularly helpful for teaching communication dynamics. These videos are set in the context of academic research discussions, though the scenarios are broad enough to facilitate discussions for other collaborative contexts as well. The videos and associated resources are designed to be incorporated into existing curricula related to collaboration.
Julia Sharp is an associate professor of statistics and the Director of the Graybill Statistics and Data Science Laboratory at Colorado State University. Julia is a widely recognized expert in statistical collaboration and recently was awarded the Outstanding Mentor Award from ASA's Section on Statistical Consulting. When she is not working, Julia enjoys baking, hiking, and enjoying the company of family and friends.
Emily Griffith is an associate research professor of statistics at North Carolina State University. She is also a Fellow in the Office of Research Innovation working on development and strategy to further innovation in the university’s data sciences initiatives. In her free time, Emily enjoys running (even in the summer in NC), cooking, and hanging out with her family.
Megan Higgs has worked as a collaborative statistician in academia and private industry, and is now working independently as Critical Inference LLC and writing posts for a blog of the same name. She currently volunteers as editor of the International Statistical Institute’s “Statisticians React to the News” blog and serves on the ASA’s Climate Change Committee. Megan loves spending time with her family and pets in Montana.
The webinar will take place on Tuesday, September 21st from 4:00-4:45pm ET.
Registration is required but is free:
We hope that you can join what promises to be an informative discussion at the heart of our discipline.
Leigh Johnson (Capital University) and Nick Horton (Amherst College)
Moderators, CAUSE/JSDSE Webinar Series
Beitzel Professor of Technology and Society (Statistics and Data Science)
Please join us for the next CAUSE webinar Tuesday, May 27th at 4:00PM ET.
Title: What makes a good statistical question?
Presenters: Pip Arnold (New Zealand) & Chris Franklin (ASA K-12 Statistics Ambassador/ASA Fellow/UGA Emerita)
Date and Time: Tuesday, May 27, 2021
Abstract: 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 (https://www.tandfonline.com/doi/full/10.1080/26939169.2021.1877582). 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.
Registration link: https://psu.zoom.us/webinar/register/WN_0gjOKtGuQ3iws_BCI6egtA<https://psu.zoom.us/webinar/register/WN_sVBPlOp9QXWLi7SxwcBIMw>
Welcome! You are invited to join a webinar: What makes a good statistical question?. After registering, you will receive a confirmation email about joining the webinar.<https://psu.zoom.us/webinar/register/WN_0gjOKtGuQ3iws_BCI6egtA>
The CAUSE Cartoon Caption Contest for August is now taking entries!
The Consortium for the Advancement of Undergraduate Statistics Education is happy to announce our 63nd Cartoon Caption Contest – now ongoing every month for over five years! Each month a cartoon, drawn by British cartoonist John Landers, is posted for you and your students to suggest statistical captions (cartoons are posted at the beginning of the month and submissions are due at the end of the month). The caption contest is offered as a fun way to get your students thinking independently about statistical concepts.
The next cartoon and the entry rules for the contest ending August 31 are at
The best submission will be posted on CAUSEweb and the winner(s) will receive their choice of a coffee mug or t-shirt imprinted with the final cartoon.
July Results: The July caption contest featured a person sailing. The sail on the boat shows a design that looks like a comparative time series plot. Meanwhile, there is group of sharks circling the boat and a very menacing storm is seen in the distance. The winning sign/caption for the June contest was “Graphs highlight sail-ient features of the data,” written by Larry Lesser from The University of Texas at El Paso. Larry’s caption can be used to highlight various features of the times series plots shown such as the seasonal trends perhaps signaling the oncoming storm in the cartoon. An honorable mention this month goes to Jim Alloway from EMSQ Associates for the caption: “When analyzing your data, always be on the lookout for unexpected interactions,” which can be used by instructors for discussing the idea of possible synergistic effects seen with interacting factors.
Thanks to everyone who submitted a caption and congratulations to our winners!