Blase Ur - Statistics for HCI Researchers
In this talk, we will provide a high-level overview of the use of statistical testing in quantitative HCI research. We will focus on choosing statistical tests appropriate for different types of experiment designs and types of data, as well as how to characterize conclusions from those tests. No prior knowledge of applied statistics is assumed, but attendees with some prior exposure will stand to gain the most from this seminar. The presenter will give selected examples in R; again, no prior knowledge of R is required.
Blase Ur is Neubauer Family Assistant Professor of Computer Science at the University of Chicago, where he researches security, privacy, human-computer interaction, and ethical AI. He directs the UChicago SUPERgroup, which uses data-driven methods to help users make better security and privacy decisions, as well as to improve the usability of complex computer systems. He has received an NSF CAREER Award (2021), three best paper awards (CHI 2017, USENIX Security 2016, UbiComp 2014) and five honorable mention paper awards (CHI 2021, CHI 2021, CHI 2020, CHI 2016, CHI 2012). His research has been covered in the NY Times, Forbes, and Ars Technica. He received the 2020 Allen Newell Award for Research Excellence, the 2018 SIGCHI Outstanding Dissertation Award, the 2018 IEEE Cybersecurity Award for Practice, the 2016 John Karat Usable Privacy and Security Student Research Award, an NDSEG fellowship, and a Fulbright scholarship. He holds degrees from Carnegie Mellon University (PhD and MS) and Harvard University (AB).