This course teaches the principles for designing physics experiments and analyzing data to obtain robust results. It explores the choice of experimental methods and conditions used for data collection and examines important techniques used for data analysis. Topics include: experimental and numerical noise/background sources, characteristics, and mitigation; sampling, replicates, and controls; probability distributions; parameter estimation; error estimation and confidence levels; model selection, model fitting, and hypothesis testing; non-parametric analyses; applications of frequentist and Bayesian statistics; modes of failure in measurements and analysis.
Section | Capacity | Class times | Login to view Instructor(s) and Location |
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LECTURE B01
(78251) |
18 |
2025-01-06 - 2025-04-09 (MWF)
11:00 - 11:50
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