The U.S. Conference on Teaching Statistics (USCOTS)<https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.cause…> will be held at the Penn Stater in State College, PA from Thursday, June 1st through Saturday June 3rd, 2023, with pre-conference workshops starting on Tuesday, May 30th. This conference provides a welcoming and engaging (perhaps even fun!) environment in which teachers can exchange ideas and motivate each other to improve their teaching of statistics. The conference features thought-provoking plenary sessions, interactive breakout sessions, informative posters-and-beyond sessions, and opening and closing sessions with inspiring and lively five-minute presentations. Other highlights include birds-of-a-feather discussions, a speed mentoring session, an awards ceremony, extensive pre-conference workshops, and exhibitor technology demonstrations.
The USCOTS theme for 2023 is "Communicating with/about Data." Sessions will explore many aspects of this theme, including teaching students to present data-driven arguments through words, visualizations, and even code, and helping teachers effectively communicate with their students as they develop their understanding of key statistical ideas.
USCOTS has been held in odd-numbered years since 2005. While USCOTS 2021 was held virtually due to COVID-19, all sessions of USCOTS 2023 will be held in–person. Please consider attending and even better, consider submitting a proposal<https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.cause…> for an interactive breakout session, pre-conference workshop, “posters and beyond” contribution, or “birds of a feather” discussion topic. Deadlines are:
· November 30th, 2022 for proposing a pre-conference workshop
· November 30th, 2022 for proposing an interactive breakout session
· January 29th, 2023 for proposing a “posters and beyond” contribution, if you would like to receive formative feedback before your final submission
· March 5th, 2023 for final submission of proposals for a “posters and beyond” contribution, whether or not you submitted a version earlier for feedback
· May 6th, 2023 for proposing a “birds of a feather” discussion
USCOTS 2023 will also feature a Research Satellite, to be held on May 31st and June 1st, that brings together researchers in statistics and data science education to promote and support projects of common interest.
You can find more information about the conference and its theme, along with proposal submission links, here:
Please address questions to program co-chairs Allan Rossman (arossman(a)calpoly.edu<mailto:firstname.lastname@example.org>) or Kelly McConville (kmcconville(a)fas.harvard.edu<mailto:email@example.com>), or to CAUSE director Dennis Pearl (dkp13(a)psu.edu<mailto:firstname.lastname@example.org>).
Good morning! CAUSE Research Reading Group meetings continue! Our next
meeting is scheduled on *Thursday, December 1st*, at *4:00 – 5:00 PM ET*.
Please find the link for registration and the link to the article below.
*Article: *Hicks & Peng (2019): "*Elements and Principles of Data Analysis*",
Zoom: Register in advance for this meeting:
After registering, you will receive a confirmation email containing
information about joining the meeting.
Our host for this session will be Abhishek Chakraborty (thank you so much,
Abhishek!). Believe or not, this is going to be our second-to-last session
for this reading series. Please come join us!
Look forward to having you join us next Thursday,
Shu-Min & Megan
The Consortium for the Advancement of Undergraduate Statistics Education is happy to announce our 78th Cartoon Caption Contest! 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 November 30 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.
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The October caption contest cartoon is shown here. The judges found the winning caption to be “Unfortunately the Tailor's approximation didn't lead to a goodness of fit," written by Eric Vance, from University of Colorado in Boulder. Eric’s clever pun can be a vehicle to discuss the value of approximations in statistical inference and the need to check the fit of models. An honorable mention this month goes to Stephen Walsh, a student at Virginia Tech for his caption, “Significant bias is never in style.” Stephen’s caption can help in teaching about avoiding highly biased inference but at the same time being open to some bias if it helps to improve reliability. A second honorable mention goes to Louis Rocconi from University of Tennessee for the caption “Now that’s what I call measurement error!” as vehicle to discuss that topic.
Thanks to everyone who submitted a caption and congratulations to our winners!
Many new principles and standards have been developed to facilitate cultural changes in fostering reproducible research, but less so has been done in teaching. To highlight work in this important and developing area, the Journal of Statistics and Data Science Education invited papers related to "Teaching reproducibility and responsible workflow". The November 2022 issue of the journal is devoted to this topic (see https://www.tandfonline.com/toc/ujse21/30/3<https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.tandf…>). We are excited by the opportunities and options brought forward in these 11 papers. This webinar will include an overview of the special issue that is intended to provide motivation, guidance, and examples that help the data science and statistics education community better inculcate these increasingly important research-based practices. The webinar will include an opportunity for Q&A with the audience focused on next steps and ways to move forward.
Presented By: Aneta Piekut (University of Sheffield), Colin Rundel (Duke University), Micaela Parker (Academic Data Science Alliance), Nicholas J. Horton (Amherst College), and Rohan Alexander (University of Toronto)
Aneta Piekut is Senior Lecturer in Quantitative Social Sciences at the Sheffield Methods Institute, University of Sheffield. She is an associate editor of the Journal of Statistics and Data Science Education.
Colin Rundel is 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. Colin was a guest editor of the special issue on teaching reproducibility and responsible workflow.
Micaela Parker is the Founder and Executive Director of the Academic Data Science Alliance (ADSA), https://academicdatascience.org/data-science/about<https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Facademicd…>. Micaela was a guest editor of the special issue on teaching reproducibility and responsible workflow.
Nicholas J. Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. He is the editor of the Journal of Statistics and Data Science Education and is the co-chair of the National Academies Committee on Applied and Theoretical Statistics (CATS).
Rohan Alexander is an assistant professor at the University of Toronto jointly appointed in the Faculty of Information and the Department of Statistical Sciences. He is an associate editor of the Journal of Statistics and Data Science Education.
The webinar will take place on December 13, 2022, from 4:00-4:45 pm EST.
Registration is required but is free. https://causeweb.org/cause/webinar/jsdse/2022-12
We hope that you can join what promises to be an informative discussion about teaching data science and statistics.
Leigh Johnson (Capital University)
Moderator, CAUSE/JSDSE Webinar Series
I am excited to announce that Montana State University (in beautiful Bozeman, MT) is hiring for two positions. Our department offers an emphasis in Statistics Education in our Statistics PhD program, and currently I’m the only statistics education faculty in the Statistics group (of eight faculty) in our department. We would be thrilled if we could fill one of these positions with someone in Statistics or Data Science Education!
The first position in particular lines up well with the area of Statistics Education – it is part of the College of Letters & Science “Cohort hiring initiative” to support a diverse student body, faculty and staff. This initiative (https://www.montana.edu/lettersandscience/cohorthire/) is looking for candidates that "focus on the wellness of underserved communities and whose scholarship may speak but is not limited to rural communities, the environment, community empowerment, community sustainability (environmental, governmental, or otherwise), health disparities (racial, gender, or rural), climate, and teaching pedagogy" (https://jobs.montana.edu/postings/32831<https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjobs.mont…>). Applications received by November 17, 2022 will receive full consideration for the cohort hire position.
The second position is specific to the Department of Mathematical Sciences and is looking for a "scholar with research interests in computational and applied mathematics, including the mathematics of machine learning and data science" (https://jobs.montana.edu/postings/32779<https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjobs.mont…>). Applications received by November 30, 2022 will receive full consideration for the applied math/data science position.
If you have any graduate students on the job market or know of anyone else who may be interested, please spread the word widely! Happy to answer any questions (stacey.hancock(a)montana.edu<mailto:email@example.com>).
Associate Professor of Statistics
Montana State University | Department of Mathematical Sciences
2-195 Wilson Hall | P.O. Box 172400
Bozeman, MT 59717-2400
"Implementing a Senior Statistics Practicum: Lessons and Feedback from Multiple Offerings", CAUSE/JSDSE webinar series (free, signup required)
Title: Implementing a Senior Statistics Practicum: Lessons and Feedback from Multiple Offerings
Tuesday, November 15th, 2022
4:00 pm – 4:30 pm ET
Presented by: Kirsten Doehler (Elon University)
Abstract: This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) / JSDSE (Journal of Statistics and Data Science Education) webinar series, we highlight the article, Implementing a Senior Statistics Practicum: Lessons and Feedback from Multiple Offerings.
A Statistics Practicum course can be offered as another option besides an internship or research experience for students to fulfill a required statistics major capstone experience.
This webinar will discuss the first and fourth offering of this practicum course, which provided a unique perspective on the initial implementation of the course and its development over time.
The course offered students opportunities to carry out statistical consulting projects with external clients.
Students were given multiple reflection assignments throughout the course.
Challenges of the projects were discussed in the reflections, which included issues of data cleaning and analysis. Students also responded to both Likert-scale and open-ended questions on an end of semester survey. These responses provided information on sentiment regarding the consulting projects and perceived usefulness of various components of the Statistics Practicum course. Both student reflection assignments and survey responses were analyzed as part of this study. Explanations of the thought processes that went into setting up and running the course, as well as advice and suggestions for course improvements and successful administration, will be discussed.
Registration is free but required: https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.cause…
Article Link: https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.tandf…
The Department of Statistics and Probability at Michigan State University is conducting a search for a faculty position that is devoted to teaching statistics and data science.
If you or anyone you know might be interested in such a position, here’s a link to the job posting: https://careers.msu.edu/en-us/job/512407/specialist-teachercontinuing<https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcareers.m…>
The position is in the “continuing academic specialist” system at MSU, which is a very good option for those who are interested in a teaching-focused career. Faculty members in this system have job security, with initial appointments of a few years and then once they have a successful review (after about six years) their appointment does not have an end date. In this sense it is similar to a tenure-system appointment, with reasonably similar job security. There is also good potential for career advancement, with an opportunity for promotion to senior specialist (similar to promotion to full professor), and also opportunities to move into administration. (For example in my college at MSU there are assistant deans and program directors who are in the continuing academic specialist system.) So this is a solid career path for someone who is interested in instruction (and possibly administration) at the college level.
We have a great group of teaching-focused faculty members in the Stat Department at MSU, and also more broadly within the university, so there’s a large, engaged, supportive community to interact with.
Please send any questions about the posting to Camille Fairbourn (fairbour(a)msu.edu<mailto:firstname.lastname@example.org>), who would be happy to talk to anyone who might be interested in this sort of position. It requires a master’s degree (or PhD) in statistics (or a related field such as mathematics, computer science, data science, etc.).