ASA Connect

Dear Colleagues, 

Happy 2024!   The ASA Statistical Learning and Data Science Section is pleased to announce its first webinar in 2024 series, presented by Dr. Andrew Gelman.  Andrew will discuss about how we can learn from our mistakes to structure the workflow more efficiently.  Hope to see you there!

Title:                         Learning from mistakes

Speakers:                Dr. Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University

Date and Time:       January 30, 2024, 2:30 to 4:00 pm Eastern Time

Registration Link:   ASA SLDS Webinar Registration Link [eventbrite.com]

Abstract:                 We learn so much from mistakes! How can we structure our workflow so that we can learn from mistakes more effectively? I will discuss a bunch of examples where I have learned from mistakes, including data problems, coding mishaps, errors in mathematics, and conceptual errors in theory and applications. I will also discuss situations where researchers have avoided good learning opportunities. We can then try to use all these cases to develop some general understanding of how and when we learn from errors in the context of the fractal nature of scientific revolutions.

Presenter:                Andrew Gelman is a professor of statistics and political science at Columbia University. He has received the Outstanding Statistical Application award three times from the American Statistical Association, the award for best article published in the American Political Science Review, the Mitchell and DeGroot prizes from the International Society of Bayesian Analysis, and the Council of Presidents of Statistical Societies award. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin), Teaching Statistics: A Bag of Tricks (with Deborah Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina), and Regression and Other Stories (with Jennifer Hill and Aki Vehtari).

Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; the effects of incumbency and redistricting; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.

If this is online, how can it be sold out?

Dear Colleagues, 

Happy 2024!   The ASA Statistical Learning and Data Science Section is pleased to announce its first webinar in 2024 series, presented by Dr. Andrew Gelman.  Andrew will discuss about how we can learn from our mistakes to structure the workflow more efficiently.  Hope to see you there!

Title:                         Learning from mistakes

Speakers:                Dr. Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University

Date and Time:       January 30, 2024, 2:30 to 4:00 pm Eastern Time

Registration Link:   ASA SLDS Webinar Registration Link [eventbrite.com]

Abstract:                 We learn so much from mistakes! How can we structure our workflow so that we can learn from mistakes more effectively? I will discuss a bunch of examples where I have learned from mistakes, including data problems, coding mishaps, errors in mathematics, and conceptual errors in theory and applications. I will also discuss situations where researchers have avoided good learning opportunities. We can then try to use all these cases to develop some general understanding of how and when we learn from errors in the context of the fractal nature of scientific revolutions.

Presenter:                Andrew Gelman is a professor of statistics and political science at Columbia University. He has received the Outstanding Statistical Application award three times from the American Statistical Association, the award for best article published in the American Political Science Review, the Mitchell and DeGroot prizes from the International Society of Bayesian Analysis, and the Council of Presidents of Statistical Societies award. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin), Teaching Statistics: A Bag of Tricks (with Deborah Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina), and Regression and Other Stories (with Jennifer Hill and Aki Vehtari).

Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; the effects of incumbency and redistricting; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.