2010/11/30 by Frederik Beaujean, A. Caldwell, Allen Caldwell +4 · 1 citation
Decision Sciences · Mathematics · Physics and Astronomy · #Probabilistic and Robust Engineering Design #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #physics.data-an
paper · pdf · doi:10.1103/physrevd.83.012004
33 pages, 13 figures; added figure for runs test, changed to coherent notation, updated bibliography; fixed formula in the appendix
openalex publication_date 2011/01/25 · arxiv created 2011/05/23 · arxiv updated 2013/05/29 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Deciding whether a model provides a good description of data is often based on a goodness-of-fit criterion summarized by a p-value. Although there is considerable confusion concerning the meaning of p-values, leading to their misuse, they are nevertheless of practical importance in common data analysis tasks. We motivate their application using a Bayesian argumentation. We then describe commonly and less commonly known discrepancy variables and how they are used to define p-values. The distribution of these are then extracted for examples modeled on typical data analysis tasks, and comments on their usefulness for determining goodness-of-fit are given.