Simple and statistically sound recommendations for analysing physical theories
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Published:2022-04-29
Issue:5
Volume:85
Page:052201
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ISSN:0034-4885
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Container-title:Reports on Progress in Physics
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language:
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Short-container-title:Rep. Prog. Phys.
Author:
AbdusSalam Shehu S, Agocs Fruzsina J, Allanach Benjamin C, Athron PeterORCID, Balázs CsabaORCID, Bagnaschi Emanuele, Bechtle Philip, Buchmueller Oliver, Beniwal Ankit, Bhom Jihyun, Bloor Sanjay, Bringmann Torsten, Buckley Andy, Butter Anja, Camargo-Molina José Eliel, Chrzaszcz Marcin, Conrad Jan, Cornell Jonathan M, Danninger Matthias, de Blas Jorge, De Roeck Albert, Desch Klaus, Dolan Matthew, Dreiner Herbert, Eberhardt Otto, Ellis John, Farmer Ben, Fedele Marco, Flächer Henning, Fowlie AndrewORCID, Gonzalo Tomás E, Grace Philip, Hamer Matthias, Handley Will, Harz Julia, Heinemeyer Sven, Hoof SebastianORCID, Hotinli Selim, Jackson Paul, Kahlhoefer Felix, Kowalska Kamila, Krämer Michael, Kvellestad Anders, Martinez Miriam Lucio, Mahmoudi Farvah, Santos Diego Martinez, Martinez Gregory D, Mishima Satoshi, Olive Keith, Paul Ayan, Prim Markus Tobias, Porod Werner, Raklev Are, Renk Janina J, Rogan Christopher, Roszkowski Leszek, Ruiz de Austri Roberto, Sakurai Kazuki, Scaffidi Andre, Scott Pat, Sessolo Enrico Maria, Stefaniak Tim, Stöcker Patrick, Su Wei, Trojanowski Sebastian, Trotta Roberto, Sming Tsai Yue-Lin, Van den Abeele Jeriek, Valli Mauro, Vincent Aaron C, Weiglein Georg, White Martin, Wienemann Peter, Wu Lei, Zhang Yang
Abstract
Abstract
Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Our examples can be reproduced with the code publicly available at Zenodo.
Subject
General Physics and Astronomy
Cited by
8 articles.
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