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12 results on '"Dreber, Anna"'

1. Promoting Reproducibility and Replicability in Political Science

Author

Brodeur, Abel, Esterling, Kevin, Ankel-Peters, Jörg, Bueno, Natália S, Desposato, Scott, Dreber, Anna, Genovese, Federica, Green, Donald P, Hepplewhite, Matthew, de la Guardia, Fernando Hoces, Johannesson, Magnus, Kotsadam, Andreas, Miguel, Edward, Velez, Yamil R, and Young, Lauren

Subjects
Political Science, Human Society, Reproducibility, replicability, political science, Political science
Abstract

This article reviews and summarizes current reproduction and replication practices in political science. We first provide definitions for reproducibility and replicability. We then review data availability policies for 28 leading political science journals and present the results from a survey of editors about their willingness to publish comments and replications. We discuss new initiatives that seek to promote and generate high-quality reproductions and replications. Finally, we make the case for standards and practices that may help increase data availability, reproducibility, and replicability in political science.

Published
2024

2. Reproduction and replication at scale

Author

Brodeur, Abel, Dreber, Anna, Hoces de la Guardia, Fernando, and Miguel, Edward

Subjects
Biomedical and Clinical Sciences, Health Sciences, Psychology, Humans, Reproduction, Models, Biological, Biomedical and clinical sciences, Health sciences
Published
2024

3. On the trajectory of discrimination: A meta-analysis and forecasting survey capturing 44 years of field experiments on gender and hiring decisions

Author

Schaerer, Michael, du Plessis, Christilene, Nguyen, My Hoang Bao, van Aert, Robbie CM, Tiokhin, Leo, Lakens, Daniël, Clemente, Elena Giulia, Pfeiffer, Thomas, Dreber, Anna, Johannesson, Magnus, Clark, Cory J, Collaboration, Gender Audits Forecasting, and Uhlmann, Eric Luis

Subjects
Commerce, Management, Tourism and Services, Psychology, Gender Equality, Psychology and Cognitive Sciences, Social Psychology, Commerce, management, tourism and services
Abstract

A preregistered meta-analysis, including 244 effect sizes from 85 field audits and 361,645 individual job applications, tested for gender bias in hiring practices in female-stereotypical and gender-balanced as well as male-stereotypical jobs from 1976 to 2020. A “red team” of independent experts was recruited to increase the rigor and robustness of our meta-analytic approach. A forecasting survey further examined whether laypeople (n = 499 nationally representative adults) and scientists (n = 312) could predict the results. Forecasters correctly anticipated reductions in discrimination against female candidates over time. However, both scientists and laypeople overestimated the continuation of bias against female candidates. Instead, selection bias in favor of male over female candidates was eliminated and, if anything, slightly reversed in sign starting in 2009 for mixed-gender and male-stereotypical jobs in our sample. Forecasters further failed to anticipate that discrimination against male candidates for stereotypically female jobs would remain stable across the decades.

Published
2023

4. Replication games: how to make reproducibility research more systematic

Author

Brodeur, Abel, Dreber, Anna, Hoces de la Guardia, Fernando, and Miguel, Edward

Subjects
Economics, Applied Economics, Psychology, Reproducibility of Results, Research, Research Design, Research data, Research management, Scientific community, Sociology, General Science & Technology
Published
2023

5. Examining the generalizability of research findings from archival data.

Author

Delios, Andrew, Clemente, Elena, Wu, Tao, Tan, Hongbin, Wang, Yong, Gordon, Michael, Viganola, Domenico, Chen, Zhaowei, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, and Uhlmann, Eric

Subjects
archival data, context sensitivity, generalizability, reproducibility, research reliability
Abstract

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability-for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples.

Published
2022

6. Consensus-based guidance for conducting and reporting multi-analyst studies

Author

Aczel, Balazs, Szaszi, Barnabas, Nilsonne, Gustav, van den Akker, Olmo R, Albers, Casper J, van Assen, Marcel ALM, Bastiaansen, Jojanneke A, Benjamin, Daniel, Boehm, Udo, Botvinik-Nezer, Rotem, Bringmann, Laura F, Busch, Niko A, Caruyer, Emmanuel, Cataldo, Andrea M, Cowan, Nelson, Delios, Andrew, van Dongen, Noah NN, Donkin, Chris, van Doorn, Johnny B, Dreber, Anna, Dutilh, Gilles, Egan, Gary F, Gernsbacher, Morton Ann, Hoekstra, Rink, Hoffmann, Sabine, Holzmeister, Felix, Huber, Juergen, Johannesson, Magnus, Jonas, Kai J, Kindel, Alexander T, Kirchler, Michael, Kunkels, Yoram K, Lindsay, D Stephen, Mangin, Jean-Francois, Matzke, Dora, Munafò, Marcus R, Newell, Ben R, Nosek, Brian A, Poldrack, Russell A, van Ravenzwaaij, Don, Rieskamp, Jörg, Salganik, Matthew J, Sarafoglou, Alexandra, Schonberg, Tom, Schweinsberg, Martin, Shanks, David, Silberzahn, Raphael, Simons, Daniel J, Spellman, Barbara A, St-Jean, Samuel, Starns, Jeffrey J, Uhlmann, Eric Luis, Wicherts, Jelte, and Wagenmakers, Eric-Jan

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Health Sciences, Consensus, Data Analysis, Datasets as Topic, Research, multi-analyst, metascience, statistical practice, science forum, expert consensus, analytical variability, None, medicine, neuroscience, none, Biochemistry and Cell Biology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.

Published
2021

7. Many Labs 5: Testing Pre-Data-Collection Peer Review as an Intervention to Increase Replicability

Author

Ebersole, Charles R, Mathur, Maya B, Baranski, Erica, Bart-Plange, Diane-Jo, Buttrick, Nicholas R, Chartier, Christopher R, Corker, Katherine S, Corley, Martin, Hartshorne, Joshua K, IJzerman, Hans, Lazarević, Ljiljana B, Rabagliati, Hugh, Ropovik, Ivan, Aczel, Balazs, Aeschbach, Lena F, Andrighetto, Luca, Arnal, Jack D, Arrow, Holly, Babincak, Peter, Bakos, Bence E, Baník, Gabriel, Baskin, Ernest, Belopavlović, Radomir, Bernstein, Michael H, Białek, Michał, Bloxsom, Nicholas G, Bodroža, Bojana, Bonfiglio, Diane BV, Boucher, Leanne, Brühlmann, Florian, Brumbaugh, Claudia C, Casini, Erica, Chen, Yiling, Chiorri, Carlo, Chopik, William J, Christ, Oliver, Ciunci, Antonia M, Claypool, Heather M, Coary, Sean, Čolić, Marija V, Collins, W Matthew, Curran, Paul G, Day, Chris R, Dering, Benjamin, Dreber, Anna, Edlund, John E, Falcão, Filipe, Fedor, Anna, Feinberg, Lily, Ferguson, Ian R, Ford, Máire, Frank, Michael C, Fryberger, Emily, Garinther, Alexander, Gawryluk, Katarzyna, Ashbaugh, Kayla, Giacomantonio, Mauro, Giessner, Steffen R, Grahe, Jon E, Guadagno, Rosanna E, Hałasa, Ewa, Hancock, Peter JB, Hilliard, Rias A, Hüffmeier, Joachim, Hughes, Sean, Idzikowska, Katarzyna, Inzlicht, Michael, Jern, Alan, Jiménez-Leal, William, Johannesson, Magnus, Joy-Gaba, Jennifer A, Kauff, Mathias, Kellier, Danielle J, Kessinger, Grecia, Kidwell, Mallory C, Kimbrough, Amanda M, King, Josiah PJ, Kolb, Vanessa S, Kołodziej, Sabina, Kovacs, Marton, Krasuska, Karolina, Kraus, Sue, Krueger, Lacy E, Kuchno, Katarzyna, Lage, Caio Ambrosio, Langford, Eleanor V, Levitan, Carmel A, de Lima, Tiago Jessé Souza, Lin, Hause, Lins, Samuel, Loy, Jia E, Manfredi, Dylan, Markiewicz, Łukasz, Menon, Madhavi, Mercier, Brett, Metzger, Mitchell, Meyet, Venus, Millen, Ailsa E, Miller, Jeremy K, and Montealegre, Andres

Subjects
Psychology, Cognitive and Computational Psychology, Applied and Developmental Psychology, Behavioral and Social Science, replication, reproducibility, metascience, peer review, Registered Reports, open data, preregistered, Applied and developmental psychology, Cognitive and computational psychology
Abstract

Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p

Published
2020

8. Variability in the analysis of a single neuroimaging dataset by many teams

Author

Botvinik-Nezer, Rotem, Holzmeister, Felix, Camerer, Colin F, Dreber, Anna, Huber, Juergen, Johannesson, Magnus, Kirchler, Michael, Iwanir, Roni, Mumford, Jeanette A, Adcock, R Alison, Avesani, Paolo, Baczkowski, Blazej M, Bajracharya, Aahana, Bakst, Leah, Ball, Sheryl, Barilari, Marco, Bault, Nadège, Beaton, Derek, Beitner, Julia, Benoit, Roland G, Berkers, Ruud MWJ, Bhanji, Jamil P, Biswal, Bharat B, Bobadilla-Suarez, Sebastian, Bortolini, Tiago, Bottenhorn, Katherine L, Bowring, Alexander, Braem, Senne, Brooks, Hayley R, Brudner, Emily G, Calderon, Cristian B, Camilleri, Julia A, Castrellon, Jaime J, Cecchetti, Luca, Cieslik, Edna C, Cole, Zachary J, Collignon, Olivier, Cox, Robert W, Cunningham, William A, Czoschke, Stefan, Dadi, Kamalaker, Davis, Charles P, Luca, Alberto De, Delgado, Mauricio R, Demetriou, Lysia, Dennison, Jeffrey B, Di, Xin, Dickie, Erin W, Dobryakova, Ekaterina, Donnat, Claire L, Dukart, Juergen, Duncan, Niall W, Durnez, Joke, Eed, Amr, Eickhoff, Simon B, Erhart, Andrew, Fontanesi, Laura, Fricke, G Matthew, Fu, Shiguang, Galván, Adriana, Gau, Remi, Genon, Sarah, Glatard, Tristan, Glerean, Enrico, Goeman, Jelle J, Golowin, Sergej AE, González-García, Carlos, Gorgolewski, Krzysztof J, Grady, Cheryl L, Green, Mikella A, Guassi Moreira, João F, Guest, Olivia, Hakimi, Shabnam, Hamilton, J Paul, Hancock, Roeland, Handjaras, Giacomo, Harry, Bronson B, Hawco, Colin, Herholz, Peer, Herman, Gabrielle, Heunis, Stephan, Hoffstaedter, Felix, Hogeveen, Jeremy, Holmes, Susan, Hu, Chuan-Peng, Huettel, Scott A, Hughes, Matthew E, Iacovella, Vittorio, Iordan, Alexandru D, Isager, Peder M, Isik, Ayse I, Jahn, Andrew, Johnson, Matthew R, Johnstone, Tom, Joseph, Michael JE, Juliano, Anthony C, Kable, Joseph W, Kassinopoulos, Michalis, Koba, Cemal, and Kong, Xiang-Zhen

Subjects
Generic health relevance, Brain, Data Analysis, Data Science, Datasets as Topic, Female, Functional Neuroimaging, Humans, Logistic Models, Magnetic Resonance Imaging, Male, Meta-Analysis as Topic, Models, Neurological, Reproducibility of Results, Research Personnel, Software, General Science & Technology
Abstract

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Published
2020

9. Variability in the analysis of a single neuroimaging dataset by many teams.

Author

Botvinik-Nezer, Rotem, Holzmeister, Felix, Camerer, Colin F, Dreber, Anna, Huber, Juergen, Johannesson, Magnus, Kirchler, Michael, Iwanir, Roni, Mumford, Jeanette A, Adcock, R Alison, Avesani, Paolo, Baczkowski, Blazej M, Bajracharya, Aahana, Bakst, Leah, Ball, Sheryl, Barilari, Marco, Bault, Nadège, Beaton, Derek, Beitner, Julia, Benoit, Roland G, Berkers, Ruud MWJ, Bhanji, Jamil P, Biswal, Bharat B, Bobadilla-Suarez, Sebastian, Bortolini, Tiago, Bottenhorn, Katherine L, Bowring, Alexander, Braem, Senne, Brooks, Hayley R, Brudner, Emily G, Calderon, Cristian B, Camilleri, Julia A, Castrellon, Jaime J, Cecchetti, Luca, Cieslik, Edna C, Cole, Zachary J, Collignon, Olivier, Cox, Robert W, Cunningham, William A, Czoschke, Stefan, Dadi, Kamalaker, Davis, Charles P, Luca, Alberto De, Delgado, Mauricio R, Demetriou, Lysia, Dennison, Jeffrey B, Di, Xin, Dickie, Erin W, Dobryakova, Ekaterina, Donnat, Claire L, Dukart, Juergen, Duncan, Niall W, Durnez, Joke, Eed, Amr, Eickhoff, Simon B, Erhart, Andrew, Fontanesi, Laura, Fricke, G Matthew, Fu, Shiguang, Galván, Adriana, Gau, Remi, Genon, Sarah, Glatard, Tristan, Glerean, Enrico, Goeman, Jelle J, Golowin, Sergej AE, González-García, Carlos, Gorgolewski, Krzysztof J, Grady, Cheryl L, Green, Mikella A, Guassi Moreira, João F, Guest, Olivia, Hakimi, Shabnam, Hamilton, J Paul, Hancock, Roeland, Handjaras, Giacomo, Harry, Bronson B, Hawco, Colin, Herholz, Peer, Herman, Gabrielle, Heunis, Stephan, Hoffstaedter, Felix, Hogeveen, Jeremy, Holmes, Susan, Hu, Chuan-Peng, Huettel, Scott A, Hughes, Matthew E, Iacovella, Vittorio, Iordan, Alexandru D, Isager, Peder M, Isik, Ayse I, Jahn, Andrew, Johnson, Matthew R, Johnstone, Tom, Joseph, Michael JE, Juliano, Anthony C, Kable, Joseph W, Kassinopoulos, Michalis, Koba, Cemal, and Kong, Xiang-Zhen

Subjects
Brain, Humans, Magnetic Resonance Imaging, Logistic Models, Reproducibility of Results, Models, Neurological, Software, Research Personnel, Female, Male, Meta-Analysis as Topic, Functional Neuroimaging, Datasets as Topic, Data Science, Data Analysis, General Science & Technology
Abstract

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Published
2020

10. Crowdsourcing hypothesis tests: Making transparent how design choices shape research results.

Author

Landy, Justin F, Jia, Miaolei Liam, Ding, Isabel L, Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R, Gronau, Quentin F, Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M, Bauman, Christopher W, Brady, William J, Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M Brent, Hahn, Adam, Hall, Michael P, Jiménez-Leal, William, Johnson, David J, Lucas, Richard E, Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A, Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M, Van Bavel, Jay J, Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A, Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, and Uhlmann, Eric L

Subjects
conceptual replications, crowdsourcing, forecasting, research robustness, scientific transparency, Crowdsourcing Hypothesis Tests Collaboration, Crowdsourcing, stimulus sampling, replication, Psychology, Cognitive Sciences, Marketing, Social Psychology
Abstract

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from 2 separate large samples (total N > 15,000) were then randomly assigned to complete 1 version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: Materials from different teams rendered statistically significant effects in opposite directions for 4 of 5 hypotheses, with the narrowest range in estimates being d = -0.37 to + 0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for 2 hypotheses and a lack of support for 3 hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, whereas considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Published
2020

11. Redefine statistical significance.

Author

Benjamin, Daniel J, Berger, James O, Johannesson, Magnus, Nosek, Brian A, Wagenmakers, E-J, Berk, Richard, Bollen, Kenneth A, Brembs, Björn, Brown, Lawrence, Camerer, Colin, Cesarini, David, Chambers, Christopher D, Clyde, Merlise, Cook, Thomas D, De Boeck, Paul, Dienes, Zoltan, Dreber, Anna, Easwaran, Kenny, Efferson, Charles, Fehr, Ernst, Fidler, Fiona, Field, Andy P, Forster, Malcolm, George, Edward I, Gonzalez, Richard, Goodman, Steven, Green, Edwin, Green, Donald P, Greenwald, Anthony G, Hadfield, Jarrod D, Hedges, Larry V, Held, Leonhard, Hua Ho, Teck, Hoijtink, Herbert, Hruschka, Daniel J, Imai, Kosuke, Imbens, Guido, Ioannidis, John PA, Jeon, Minjeong, Jones, James Holland, Kirchler, Michael, Laibson, David, List, John, Little, Roderick, Lupia, Arthur, Machery, Edouard, Maxwell, Scott E, McCarthy, Michael, Moore, Don A, Morgan, Stephen L, Munafó, Marcus, Nakagawa, Shinichi, Nyhan, Brendan, Parker, Timothy H, Pericchi, Luis, Perugini, Marco, Rouder, Jeff, Rousseau, Judith, Savalei, Victoria, Schönbrodt, Felix D, Sellke, Thomas, Sinclair, Betsy, Tingley, Dustin, Van Zandt, Trisha, Vazire, Simine, Watts, Duncan J, Winship, Christopher, Wolpert, Robert L, Xie, Yu, Young, Cristobal, Zinman, Jonathan, and Johnson, Valen E

Subjects
Data Interpretation, Statistical, Models, Statistical, Reproducibility of Results, Scholarly Communication
Abstract

We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.

Published
2018

12. Redefine statistical significance.

Author

Benjamin, Daniel J, Berger, James O, Johannesson, Magnus, Nosek, Brian A, Wagenmakers, E-J, Berk, Richard, Bollen, Kenneth A, Brembs, Björn, Brown, Lawrence, Camerer, Colin, Cesarini, David, Chambers, Christopher D, Clyde, Merlise, Cook, Thomas D, De Boeck, Paul, Dienes, Zoltan, Dreber, Anna, Easwaran, Kenny, Efferson, Charles, Fehr, Ernst, Fidler, Fiona, Field, Andy P, Forster, Malcolm, George, Edward I, Gonzalez, Richard, Goodman, Steven, Green, Edwin, Green, Donald P, Greenwald, Anthony G, Hadfield, Jarrod D, Hedges, Larry V, Held, Leonhard, Hua Ho, Teck, Hoijtink, Herbert, Hruschka, Daniel J, Imai, Kosuke, Imbens, Guido, Ioannidis, John PA, Jeon, Minjeong, Jones, James Holland, Kirchler, Michael, Laibson, David, List, John, Little, Roderick, Lupia, Arthur, Machery, Edouard, Maxwell, Scott E, McCarthy, Michael, Moore, Don A, Morgan, Stephen L, Munafó, Marcus, Nakagawa, Shinichi, Nyhan, Brendan, Parker, Timothy H, Pericchi, Luis, Perugini, Marco, Rouder, Jeff, Rousseau, Judith, Savalei, Victoria, Schönbrodt, Felix D, Sellke, Thomas, Sinclair, Betsy, Tingley, Dustin, Van Zandt, Trisha, Vazire, Simine, Watts, Duncan J, Winship, Christopher, Wolpert, Robert L, Xie, Yu, Young, Cristobal, Zinman, Jonathan, and Johnson, Valen E

Subjects
Data Interpretation, Statistical, Models, Statistical, Reproducibility of Results, Scholarly Communication, Data Interpretation, Statistical, Models
Abstract

We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.

Published
2018

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