Simple and statistically sound recommendations for analysing physical theories
Creators: name: AbdusSalam, Shehu S.
name: Agocs, Fruzsina J.
name: Allanach, Benjamin C.
name: Athron, Peter
name: Balázs, Csaba
name: Bagnaschi, Emanuele
name: Bechtle, Philip
name: Buchmueller, Oliver
name: Beniwal, Ankit
name: Bhom, Jihyun
name: Bloor, Sanjay
name: Bringmann, Torsten
name: Buckley, Andy
name: Butter, Anja
name: Camargo-Molina, José Eliel
name: Chrzaszcz, Marcin
name: Conrad, Jan
name: Cornell, Jonathan M.
name: Danninger, Matthias
name: de Blas, Jorge
name: De Roeck, Albert
name: Desch, Klaus
name: Dolan, Matthew
name: Dreiner, Herbert
name: Eberhardt, Otto
name: Ellis, John
name: Farmer, Ben
name: Fedele, Marco
name: Flächer, Henning
name: Fowlie, Andrew
name: Gonzalo, Tomás E.
name: Grace, Philip
name: Hamer, Matthias
name: Handley, Will
name: Harz, Julia
name: Heinemeyer, Sven
name: Hoof, Sebastian
name: Hotinli, Selim
name: Jackson, Paul
name: Kahlhoefer, Felix
name: Kowalska, Kamila
name: Krämer, Michael
name: Kvellestad, Anders
name: Martinez, Miriam Lucio
name: Mahmoudi, Farvah
name: Santos, Diego Martinez
name: Martinez, Gregory D.
name: Mishima, Satoshi
name: Olive, Keith
name: Paul, Ayan
name: Prim, Markus Tobias
name: Porod, Werner
name: Raklev, Are
name: Renk, Janina J.
name: Rogan, Christopher
name: Roszkowski, Leszek
name: de Austri, Roberto Ruiz
name: Sakurai, Kazuki
name: Scaffidi, Andre
name: Scott, Pat
name: Sessolo, Enrico Maria
name: Stefaniak, Tim
name: Stöcker, Patrick
name: Su, Wei
name: Trojanowski, Sebastian
name: Trotta, Roberto
name: Tsai, Yue-Lin Sming
name: Abeele, Jeriek Van den
name: Valli, Mauro
name: Vincent, Aaron C.
name: Weiglein, Georg
name: White, Martin
name: Wienemann, Peter
name: Wu, Lei
name: Zhang, Yang
Publisher: name: arXiv
Year: 2020
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: High Energy Physics - Experiment (hep-ex)
subject: Data Analysis, Statistics and Probability (physics.data-an)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: 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 https://doi.org/10.5281/zenodo.4322283.
description: 15 pages, 4 figures. extended discussions. closely matches version accepted for publication