Response to Comment on “Estimating the reproducibility of psychological science”

  title={Response to Comment on “Estimating the reproducibility of psychological science”},
  author={Christopher J. Anderson and {\vS}těp{\'a}n Bahn{\'i}k and Michael Barnett-Cowan and Frank Bosco and Jesse J. Chandler and Christopher R. Chartier and Felix Cheung and Cody Daniel Christopherson and Andreas Cordes and Edward J. Cremata and Nicol{\'a}s Della Penna and Vivien Estel and Anna Fedor and Stanka A. Fitneva and Michael C. Frank and James A. Grange and Joshua K. Hartshorne and Fred Hasselman and Felix Henninger and Marije van der Hulst and Kai J. Jonas and Calvin K. Lai and Carmel A. Levitan and Jeremy K. Miller and Katherine Sledge Moore and Johannes M. Meixner and Marcus Robert Munafo and Koen Ilja Neijenhuijs and Gustav Nilsonne and Brian A. Nosek and Franziska Plessow and Jason M. Prenoveau and Ashley A. Ricker and Kathleen Schmidt and Jeffrey R. Spies and Stefan Stieger and Nina Strohminger and Gavin Sullivan and Robbie C. M. Aert and Marcel A.L.M. van Assen and Wolf Vanpaemel and Michelangelo Vianello and Martin Voracek and Kellylynn Zuni},
  pages={1037 - 1037}
Gilbert et al. conclude that evidence from the Open Science Collaboration’s Reproducibility Project: Psychology indicates high reproducibility, given the study methodology. Their very optimistic assessment is limited by statistical misconceptions and by causal inferences from selectively interpreted, correlational data. Using the Reproducibility Project: Psychology data, both optimistic and pessimistic conclusions about reproducibility are possible, and neither are yet warranted. 
Evaluating Psychological Research Requires More Than Attention to the N
The article discusses research being done on the use of effect-size estimates in testing psychological theories. It references the study "Small Telescopes: Detectability and the Evaluation of
Statistical methods for replicability assessment
Large-scale replication studies like the Reproducibility Project: Psychology (RP:P) provide invaluable systematic data on scientific replicability, but most analyses and interpretations of the data
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A Statistical Model to Investigate the Reproducibility Rate Based on Replication Experiments
  • F. Pauli
  • Computer Science
    International Statistical Review
  • 2018
A statistical model is proposed to estimate the reproducibility rate and the effect of some study characteristics on its reliability, and it is suggested that the similarity between original study and the replica is not so relevant, thus mitigating some criticism directed to replication experiments.
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Suggestions for improving the reproducibility of studies in behavior science and analysis are described throughout.


Comment on “Estimating the reproducibility of psychological science”
It is shown that this article contains three statistical errors and provides no support for the conclusion that the reproducibility of psychological science is surprisingly low, and that the data are consistent with the opposite conclusion.
Estimating the reproducibility of psychological science
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.
An Open, Large-Scale, Collaborative Effort to Estimate the Reproducibility of Psychological Science
  • Brian A. Nosek, D. Lakens
  • Psychology
    Perspectives on psychological science : a journal of the Association for Psychological Science
  • 2012
The Reproducibility Project is an open, large-scale, collaborative effort to systematically examine the rate and predictors of reproducibility in psychological science.
Investigating variation in replicability: A “Many Labs” replication project
Although replication is a central tenet of science, direct replications are rare in psychology. This research tested variation in the replicability of thirteen classic and contemporary effects across
Replication and Researchers' Understanding of Confidence Intervals and Standard Error Bars.
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