In March, the ASA published a special issue of The American Statistician (TAS) related to statistical inference in the 21st century. In the initial article, Moving to a World Beyond “p < 0.05”, Wassersein, Schirm, and Lazar (2019) write for the ASA saying,
“The ASA Statement on P-Values and Statistical Significance stopped just short of recommending that declarations of “statistical significance” be abandoned. We take that step here. We conclude, based on our review of the articles in this special issue and the broader literature, that it is time to stop using the term “statistically significant” entirely.
Last week I attended the United States Conference on Teaching Statistics. The biennial conference, which took place at Penn State, attracts statistics educators and statistics education researchers from across the world. It was a fantastic conference with keynotes from Jane Watson, Allen Schirm and Ron Wasserstein, John Kruschke, and Kari Lock Morgan.
I cajoled four of my graduate students (Jonathan Brown, Mike Huberty, Chelsey Legacy, and Vimal Rao) to tag along, and it was fun to see them interacting with the people and ideas presented.
I am giving a talk at the 46th Annual Meeting of the Statistical Society of Canada in Montreal on June 05, 2018. The talk is part of an invited session on Teaching Statistics to Graduate Students in the Health and Social Sciences. Information, including the slides, is available below.
Title: Statistical Computing: Non-Ignorable Missingness in the Graduate-Level Social Science Curriculum
Abstract: In 2010, Nolan and Temple Lang pointed out that “
This post is the fourth (and last) in a series of blogposts in which I respond to questions from the students in the Becoming a Teacher of Statistics course. In today’s posting I respond to questions that asked me for predictions about the future of statistics teaching and statistics education research.
Before I get into the Q&A, let me just state: Prediction is hard. Leland Wilkinson in The Future of Statistical Computing reminded us of this when he cited a prediction about computers that Andrew Hamilton made in a 1949 issue of Popular Mechanics
My colleague Robert delMas is teaching our doctoral-level research seminar, EPsy 8271 next fall, and it looks to be an interesting topic. The details about the course follow:
EPSY 8271 | Statistics Education Research Seminar: Teaching Statistics from a Modeling Perspective (3 credits) Day/Time: Fridays, 9:00 a.m.–11:30 a.m. (Fall 2018)
Location: 220 Wulling Hall Instructor: Robert delMas, Ph.D.
This seminar will focus on research related to teaching introductory statistics through a modeling approach.
This post is the third in a series of blogposts in which I respond to questions from the students in the Becoming a Teacher of Statistics course. In today’s posting I respond to questions related to teaching.
With a flipped classroom, the professor tapes their lecture and has students view the video online. I think the logical extreme of this is that at some point, certain lectures will become immensely popular or polished to the point of surpassing local professors’ lectures.
This post is the second in a series of blogposts in which I respond to questions from the students in the Becoming a Teacher of Statistics course. In today’s posting I respond to questions related to several of the projects I have worked on over the years.
Can you tell us a little about your AIMS project? I think it’s not always easy for everyone to apply guidelines in order to change their behavior (in any context), but your AIMS project seems to fill that gap for stats educators-that is, it explicitly creates educational materials aligned with GAISE.
This last Tuesday (03/27/2018) I was invited to go do a Q&A with the students in the EPsy 5271 Becoming a Teacher of Statistics course. This class also met via video-link with a similar course at Penn State. The students asked thoughful (sometimes difficult) questions and I tried to answer them. I asked them if I could blog out their questions and my responses and they kindy said “yes”. Rather than respond to all of them at once, I thought I would use this opportunity to create several blogposts.
Adapting and Implementing Innovative Material in Statistics (AIMS) was an NSF-funded project from 2006–2010 that developed lesson plans and activities based on innovative materials that have been produced for introductory statistics courses (DUE-0535912).
Change Agents for Teaching and Learning Statistics (CATALST) was an NSF-funded project from 2008–2012 that developed materials for teaching a radically different introductory statistics course based on randomization and bootstrap methods to provide students a deep understanding of statistical inference (DUE-0814433).