January Issue of JSDSE Takes on
Difficult History of Statistics
The January issue of the open-access Journal of Statistics and Data Science Education (JSDSE) leads with a paper by Lee Kennedy-Shaffer, “Teaching the Difficult Past of Statistics to Improve the Future,” that recounts the difficult past of statistics, notably the role of eugenics in the development of the field. He argues we must confront our history to move forward and offers three examples of famous statisticians and their work on eugenics to ground the discussion. He also provides guidance for addressing the troubling history in the classroom.
Other papers published in the issue explore a number of timely topics, including the following:
Training interdisciplinary data science collaborators (Jessica L. Alzen, Ilana M. Trumble, Kimberly J. Cho, and Eric A. Vance)
Team-teaching to prepare analytics-enabled professionals (Katie A. McCarthy and Gregory A. Kuhlemeyer)
A new approach to teaching statistical inference (Mortaza Jamshidian and Parsa Jamshidian)
How students apply statistics terms to people (Lawrence M. Lesser and Martin Santos)
Factors related to student success in first-year statistics courses (Alexandr Akimov, Mirela Malin, Yermone Sargsyan, Gayrat Suyunov, and Salim Turdaliev)
Anxiety around learning R (Ainsley Miller and Kate Pyper)
Confidence disparities and pre-course coding (Janet E. Rosenbaum and Lisa C. Dierker)
An application to create, adjust, and check suitability of data sets (Christopher J. Casement and Laura A. McSweeney)
An exploration of how missing data is handled in dissertations and textbooks (Hairui Yu, Suzanne E. Perumean-Chaney, and Kathryn A. Kaiser)
The issue also includes an editorial that highlights interviews with notable statistics and data science educators and a Taylor & Francis collection of open-access interviews.