Seminars
Seminars are held monthly and are open to all interested members of the scientific community.
Unless otherwise stated, these seminars will take place on Zoom Fridays once a month at 1:30-2:30 PM ET. Some webinars will be hybrid events with an in-person component at the CMU campus.
To join, you must be subscribed to our seminar mailing list. You can also add the seminars to your calendar by subscribing to the Google Calendar below. Past recordings and information are available in the online archive and on the STAMPS YouTube Channel.
Upcoming Seminars
December 4, 2026 - WEBINAR
Sara Algeri, University of Minnesota
Title: When Pearson χ² and other standard tests miss what matters: goodness-of-fit for sparse counting experiments in physics and astronomy
Abstract: Physicists and astronomers routinely analyze photon or event counts in many narrow bins, such as X-ray spectra, where each bin holds only a handful of events. In this sparse regime, the classical χ² and likelihood-ratio statistics are the default tools for judging whether a background or source model fits. This talk introduces a unifying theoretical framework for studying this class of “divisible statistics” and identifies scenarios in which they are inadequate for goodness-of-fit. It also shows that all classical tests proposed in the literature are dominated by a class of weighted linear statistics. Finally, distribution-free tests with a known closed-form null distribution are discussed. These can be applied to many models or datasets at once without running numerical simulations.
Bio: Sara Algeri is an Associate Professor in the School of Statistics at the University of Minnesota. Her research interests mainly lie in astrostatistics, statistical inference, and goodness-of-fit. The main purpose of her work is to provide highly generalizable statistical solutions that directly address fundamental questions in the physical sciences and can, at the same time, be easily applied to any other scientific problem following a similar statistical paradigm. In line with this, motivated by problems arising in high-energy physics and astronomy, her current research focuses on statistical inference for signal detection, background uncertainty quantification, and model validation.