Course Information
INSTRUCTOR
Dr. Spencer Muse
Office Hours: SAS 5276 Tuesday 12:00 – 1:00; Ricks 303 Friday 10:30 – 11:30
Class Schedule
- March 5: Introduction; ASA Code of Ethics
- March 19: Misconduct and Allegations at UC-Berkeley Statistics
Student-led discussions begin
- March 26: Frequentist vs Bayesian: approaching the issue with non-statisticians
- April 2: The sad and often misunderstood history of the p-value.
- April 9: The history and ethics of women in clinical trials
The next two sections will focus on diversity. I ask that the two groups coordinate a little to avoid duplicating any material.
- April 16: Diversity 1: The science of workplace diversity
- April 23: Diversity 2: Racial exclusion and discrimination in science- past, present, and future.
Class Topics
- Group 1: Jingtian Bai, Andrew Giffin, Eunah Cho, Can Cui, Marschall Furman
Frequentist vs Bayesian: approaching the issues with non-statisticians. As statisticians it can be fun to argue with each other about which viewpoint is better. But how does a non-statistician decide which type of methods to use, and how do we help them make those choices? Make sure not to turn this into a Bayesian vs Frequentist debate for a statistical audience.
- Group 2: Dhrubajyoti Ghosh, Luming Chen, Joonho Gong, Kevin Gunn, Xu Han, Min Zhang
The sad and often misunderstood history of the p-value. This is one of many popular science type articles that have been written about p-values during the past few years. Explore the actual historical origins of the p-value. Lead us in a discussion that includes perspectives of both statisticians and non-statisticians regarding its usage, including recent bans or challenges to its usage. How might this debate impact basic statistics education, consulting, research, grant funding, etc?
- Group 3: Dana Johnson, Nick Kapur, Salih Koner, Dasom Lee, Jonathan Leirer, Lili Wu
The inclusion (or exclusion) of women in clinical trials. Until very recently women were excluded from the majority of clinical trials. Discuss the history of this area, and talk about the interplay and tradeoffs between the statistical and ethical issues involved.
- Group 4: Bowen Liu, Chang Liu, Alex Long, Lu Lu, Qi Ma, Anthony Weishampel
The science of workplace diversity. Review the science dealing with the impact of diverse workplaces, with particular attention to ethnic and gender diversity. What types of policies and procedures help workplaces attract and maintain diversity? What do NCSU and the Statistics Department do?
- Group 5: Suman Majumder, Matthew Miller, Robert Pehlman, Peng Saanchi, Wenli Shi, Sheng Zhang
Racial exclusion and discrimination in science- past, present, and future. Explore the history of racial discrimination in science. Examples are good! Focus on US history, but please do not feel restricted to only cover the US- especially since we are now in an era of a global economy with a global workforce. What measures are being used to prevent/avoid such discrimination, and what are obstacles to be overcome?