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Peter Raymond Attridge

Other affiliations: Georgia Gwinnett College
Bio: Peter Raymond Attridge is an academic researcher from Mercer University. The author has contributed to research in topic(s): Replication (statistics) & Reproducibility Project. The author has an hindex of 2, co-authored 2 publication(s) receiving 4577 citation(s). Previous affiliations of Peter Raymond Attridge include Georgia Gwinnett College.

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28 Aug 2015-Science
TL;DR: A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, and correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
Abstract: Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.

4,564 citations

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26 May 2016-Nature

1,908 citations

Journal ArticleDOI
Daniel J. Benjamin1, James O. Berger2, Magnus Johannesson1, Magnus Johannesson3, Brian A. Nosek4, Brian A. Nosek5, Eric-Jan Wagenmakers6, Richard A. Berk7, Kenneth A. Bollen8, Björn Brembs9, Lawrence D. Brown7, Colin F. Camerer10, David Cesarini11, David Cesarini12, Christopher D. Chambers13, Merlise A. Clyde2, Thomas D. Cook14, Thomas D. Cook15, Paul De Boeck16, Zoltan Dienes17, Anna Dreber3, Kenny Easwaran18, Charles Efferson19, Ernst Fehr20, Fiona Fidler21, Andy P. Field17, Malcolm R. Forster22, Edward I. George7, Richard Gonzalez23, Steven N. Goodman24, Edwin J. Green25, Donald P. Green26, Anthony G. Greenwald27, Jarrod D. Hadfield28, Larry V. Hedges14, Leonhard Held20, Teck-Hua Ho29, Herbert Hoijtink30, Daniel J. Hruschka31, Kosuke Imai32, Guido W. Imbens24, John P. A. Ioannidis24, Minjeong Jeon33, James Holland Jones34, Michael Kirchler35, David Laibson36, John A. List37, Roderick J. A. Little23, Arthur Lupia23, Edouard Machery38, Scott E. Maxwell39, Michael A. McCarthy21, Don A. Moore40, Stephen L. Morgan41, Marcus R. Munafò42, Shinichi Nakagawa43, Brendan Nyhan44, Timothy H. Parker45, Luis R. Pericchi46, Marco Perugini47, Jeffrey N. Rouder48, Judith Rousseau49, Victoria Savalei50, Felix D. Schönbrodt51, Thomas Sellke52, Betsy Sinclair53, Dustin Tingley36, Trisha Van Zandt16, Simine Vazire54, Duncan J. Watts55, Christopher Winship36, Robert L. Wolpert2, Yu Xie32, Cristobal Young24, Jonathan Zinman44, Valen E. Johnson18, Valen E. Johnson1 
University of Southern California1, Duke University2, Stockholm School of Economics3, Center for Open Science4, University of Virginia5, University of Amsterdam6, University of Pennsylvania7, University of North Carolina at Chapel Hill8, University of Regensburg9, California Institute of Technology10, Research Institute of Industrial Economics11, New York University12, Cardiff University13, Northwestern University14, Mathematica Policy Research15, Ohio State University16, University of Sussex17, Texas A&M University18, Royal Holloway, University of London19, University of Zurich20, University of Melbourne21, University of Wisconsin-Madison22, University of Michigan23, Stanford University24, Rutgers University25, Columbia University26, University of Washington27, University of Edinburgh28, National University of Singapore29, Utrecht University30, Arizona State University31, Princeton University32, University of California, Los Angeles33, Imperial College London34, University of Innsbruck35, Harvard University36, University of Chicago37, University of Pittsburgh38, University of Notre Dame39, University of California, Berkeley40, Johns Hopkins University41, University of Bristol42, University of New South Wales43, Dartmouth College44, Whitman College45, University of Puerto Rico46, University of Milan47, University of California, Irvine48, Paris Dauphine University49, University of British Columbia50, Ludwig Maximilian University of Munich51, Purdue University52, Washington University in St. Louis53, University of California, Davis54, Microsoft55
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.

1,218 citations

Posted Content
TL;DR: This article proposed to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005, which is the threshold used in this paper.
Abstract: We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.

1,085 citations

Journal ArticleDOI
Kurt Lejaeghere1, Gustav Bihlmayer2, Torbjörn Björkman3, Torbjörn Björkman4, Peter Blaha5, Stefan Blügel2, Volker Blum6, Damien Caliste7, Ivano E. Castelli8, Stewart J. Clark9, Andrea Dal Corso10, Stefano de Gironcoli10, Thierry Deutsch7, J. K. Dewhurst11, Igor Di Marco12, Claudia Draxl13, Claudia Draxl14, Marcin Dulak15, Olle Eriksson12, José A. Flores-Livas11, Kevin F. Garrity16, Luigi Genovese7, Paolo Giannozzi17, Matteo Giantomassi18, Stefan Goedecker19, Xavier Gonze18, Oscar Grånäs20, Oscar Grånäs12, E. K. U. Gross11, Andris Gulans13, Andris Gulans14, Francois Gygi21, D. R. Hamann22, P. J. Hasnip23, Natalie Holzwarth24, Diana Iusan12, Dominik B. Jochym25, F. Jollet, Daniel M. Jones26, Georg Kresse27, Klaus Koepernik28, Klaus Koepernik29, Emine Kucukbenli8, Emine Kucukbenli10, Yaroslav Kvashnin12, Inka L. M. Locht30, Inka L. M. Locht12, Sven Lubeck13, Martijn Marsman27, Nicola Marzari8, Ulrike Nitzsche28, Lars Nordström12, Taisuke Ozaki31, Lorenzo Paulatto32, Chris J. Pickard33, Ward Poelmans1, Matt Probert23, Keith Refson34, Keith Refson25, Manuel Richter28, Manuel Richter29, Gian-Marco Rignanese18, Santanu Saha19, Matthias Scheffler35, Matthias Scheffler14, Martin Schlipf21, Karlheinz Schwarz5, Sangeeta Sharma11, Francesca Tavazza16, Patrik Thunström5, Alexandre Tkatchenko14, Alexandre Tkatchenko36, Marc Torrent, David Vanderbilt22, Michiel van Setten18, Veronique Van Speybroeck1, John M. Wills37, Jonathan R. Yates26, Guo-Xu Zhang38, Stefaan Cottenier1 
25 Mar 2016-Science
TL;DR: A procedure to assess the precision of DFT methods was devised and used to demonstrate reproducibility among many of the most widely used DFT codes, demonstrating that the precisionof DFT implementations can be determined, even in the absence of one absolute reference code.
Abstract: The widespread popularity of density functional theory has given rise to an extensive range of dedicated codes for predicting molecular and crystalline properties. However, each code implements the formalism in a different way, raising questions about the reproducibility of such predictions. We report the results of a community-wide effort that compared 15 solid-state codes, using 40 different potentials or basis set types, to assess the quality of the Perdew-Burke-Ernzerhof equations of state for 71 elemental crystals. We conclude that predictions from recent codes and pseudopotentials agree very well, with pairwise differences that are comparable to those between different high-precision experiments. Older methods, however, have less precise agreement. Our benchmark provides a framework for users and developers to document the precision of new applications and methodological improvements.

846 citations