According to Anand Chinnakannan, there is a new way of thinking in decision intelligence: Agent-Assisted Bayesian Updating.
inference
Applications of NLP, Generative AI in Statistical Modeling and Decision-Making
For statisticians, traditional methods such as data mining, hypothesis testing, and inference have long been the backbone of analysis. These approaches remain powerful, but new tools—especially natural language processing and generative AI—make it easier to uncover insights, automate text-heavy tasks, and support decision-making at scale.
A Practicing Statistician’s Plea
Gang (John) Xie from Charles Sturt University, Australia, shares a compelling call to action for statisticians and researchers to address a critical issue in scientific research: the disconnect between statistical theory and its real-world application.
Metrology for AI in Medical Decision-Making
Scientists and statisticians address the reliability of radiology and pathology applications by using AI-based systems, but not without challenges.
Applications Wanted for Ellis R. Ott Scholarship
The Statistics Division of the American Society for Quality has $7,500 scholarships available to support students enrolled in, or accepted into, a master’s degree or higher program with a concentration in applied statistics, statistical engineering, and/or quality management.
Significance Covers COVID-19, Social Media, Rain-Predicting Tortoises
Can a pet tortoise predict when it’s about to rain? That’s the somewhat unusual question posed by Conner Jackson, winner of Significance magazine’s Statistical Excellence Award for Early Career Writing. Jackson’s article, “Pietro the Weather Tortoise and the Pursuit of Soggy Bun Prevention,” leads the line-up of feature articles in the October 2021 issue.
On Becoming Indispensable
In her latest column, Karen Kafadar asks, “How do we convince people they really want statisticians’ insight, estimation, and valid inference from data?”
Statistics, Fake News, and AI: Who’s on First?
Karen Kafadar introduces the ASA’s new Disinformation Task Force and discusses her hopes to bring statistical thinking and statisticians into the forefront to combat “fake news.”
Ambiguity: The Biggest Challenge Lies Ahead
George Cobb discusses what thoughtful statisticians know: Inference from data cannot be reduced to rules.
Training Students to Extract Value from Big Data
Recently, the Committee on Applied and Theoretical Statistics held a workshop on how to teach students to draw reliable inferences from large and complex data sets.