Instructors:
Jeffrey D. Blume, Associate Dean and Professor in the School of Data Science at University of Virginia
Megan H. Murray, Statistician at Eli Lilly and Company
Description: Second-generation p-values were recently proposed to address the well-known imperfections of classical p-values. Their implementation can largely be thought of as codifying ‘good standard practice’ for interpreting and reporting classical p-values. Second-generation pvalues maintain the favorable properties of classical p-values while emphasizing scientific relevance to expand their utility, functionality, and applicability. In particular, they can report evidence in favor of the alternative, in favor of the null hypothesis, or neither (inconclusive); they automatically incorporate an adjustment for multiple comparisons and multiple looks; they have lower false discovery rates than classical p-values; and they are easier to interpret. Second-generation p-values have been shown to work well in regularized models. They also lead to significantly improved model selection procedures in linear and generalized linear models. Also, second-generation p-values are non-denominational in the sense that they are readily applied in frequentist, likelihood and Bayesian settings.
CSP Short Course Info: https://ww3.aievolution.com/AMSTATevents/index.cfm?do=ev.viewEv&ev=1517
ASA Traveling Short Course Github: https://github.com/murraymegan/SGPV-ASA-Short-Course-2022
Jeffrey's Website: https://www.statisticalevidence.com/second-generation-p-values
Jeffrey's GitHub: https://github.com/StatEvidence