Micro-Seminar: How To Be More Uncertain: Statistical Thinking in the Age of Big Data PART ONE

Micro-Seminars have two parts. Attendance to both parts is required. Registering for the PART ONE session will automatically enroll you in the PART TWO session on Friday.

Part 1: Thursday, August 22, 2024 from 3:00 – 4:30 pm (PST)
Part 2: Friday, August 23, 2024 from 10:00 – 11:30 am (PST)

Humans love certainty, intuitive explanations, and discovering patterns. Yet our world is complicated and filled with randomness. Statistical thinking provides us with practical tools for making sense of an uncertain world. It can lead us to make surprising conclusions from data. And it also teaches us humility in the face of uncertainty.

In this micro-seminar, we will see examples of how data-driven decision making -- a cornerstone of this era of Big Data and AI systems -- can be quite difficult to get right and how thinking like a statistician can lead to powerful insights. Furthermore, we will learn how to better calibrate our own confidence level in the face of the unknown so that we can be a bit more precise about what we don't know. Join statistics professor Jacob Bien (Data Sciences and Operations) in a micro-seminar in which uncertainty will be given its due. Or at least that's the best estimate of what will happen.

Learning outcomes: - Recognize the role uncertainty plays in our world. - Learn approaches to reason better in the face of uncertainty. - Practice critical thinking in interpreting data.

Day 1: - Why might we want to be more uncertain? - Coincidences: what is surprising? - Data fishing - Paradoxes and fallacies based on sampling bias - Is your confidence well calibrated?

Day 2: - Day 1 takeaways - A look at our group's calibration results - More on calibration - Monty Hall - Medical diagnosis and conditional probability

Target audience: This is intended for anyone curious about uncertainty and how statistical thinking is useful in processing the world around us. Our approach will be non-technical given the limited time together and to encourage even those in non-mathematical fields to join. However, students should come away from the micro-seminar with useful ideas and thought-provoking examples.

Lead By: Professor Jacob Bien

Jacob Bien is a professor in the Department of Data Sciences and Operations in the Marshall School of Business. He received a B.S. in physics and a Ph.D. in statistics from Stanford University. Before joining USC, he was an assistant professor at Cornell University in the Department of Biological Statistics and Computational Biology and in the Department of Statistical Science. Dr. Bien's research focuses on statistical machine learning and in particular the development of novel methods that balance flexibility and interpretability for analyzing complex data. He combines ideas from convex optimization and statistics to develop methods that are of direct use to scientists and others with large datasets. Some of his more applied work has been focused on understanding communities of microbes living in the ocean and on using data to better respond to the COVID-19 pandemic. He is a fellow of the Institute of Mathematical Statistics, and his work has been supported by the National Science Foundation (CAREER award), the National Institutes of Health, and the Simons Foundation. He serves as an associate editor for two statistics journals and enjoys juggling in his spare time.

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