
Brian A. Nosek
VerifiedUniversity of Virginia · Psychology and Neuroscience
Active 1982–2024
Research topics
- Computer Science
- Political Science
- Statistics
- Psychology
- Mathematics
- Artificial Intelligence
- Medicine
- Data Mining
- Computer Security
- Data science
- Database
- Econometrics
- Biology
- Pathology
- Law
- Family medicine
Selected publications
RETRACTED ARTICLE: High replicability of newly discovered social-behavioural findings is achievable
Nature Human Behaviour · 2023 · 53 citations
- Psychology
- Biology
Failures to replicate evidence of new discoveries have forced scientists to ask whether this unreliability is due to suboptimal implementation of methods or whether presumptively optimal methods are not, in fact, optimal. This paper reports an investigation by four coordinated laboratories of the prospective replicability of 16 novel experimental findings using rigour-enhancing practices: confirmatory tests, large sample sizes, preregistration and methodological transparency. In contrast to past systematic replication efforts that reported replication rates averaging 50%, replication attempts here produced the expected effects with significance testing (P < 0.05) in 86% of attempts, slightly exceeding the maximum expected replicability based on observed effect sizes and sample sizes. When one lab attempted to replicate an effect discovered by another lab, the effect size in the replications was 97% that in the original study. This high replication rate justifies confidence in rigour-enhancing methods to increase the replicability of new discoveries.
Nature Communications · 2022 · 101 citations
- Computer Science
- Data Mining
- Computer Science
) is 1.0 [0.9, 1.1], range [0.3, 2.1]. While the majority of results are closely reproduced, a subset are not. The latter can be explained by incomplete reporting and updated data. Greater methodological transparency aligned with new guidance may further improve reproducibility and validity assessment, thus facilitating evidence-based decision-making. Study registration number: EUPAS19636.
Systematizing Confidence in Open Research and Evidence (SCORE)
2021 · 67 citations
- Computer Science
- Computer Science
- Data science
Assessing the credibility of research claims is a central, continuous, and laborious part of the scientific process. Credibility assessment strategies range from expert judgment to aggregating existing evidence to systematic replication efforts. Such assessments can require substantial time and effort. Research progress could be accelerated if there were rapid, scalable, accurate credibility indicators to guide attention and resource allocation for further assessment. The SCORE program is creating and validating algorithms to provide confidence scores for research claims at scale. To investigate the viability of scalable tools, teams are creating: a database of claims from papers in the social and behavioral sciences; expert and machine generated estimates of credibility; and, evidence of reproducibility, robustness, and replicability to validate the estimates. Beyond the primary research objective, the data and artifacts generated from this program will be openly shared and provide an unprecedented opportunity to examine research credibility and evidence.
Many Labs 5: Testing Pre-Data-Collection Peer Review as an Intervention to Increase Replicability
Advances in Methods and Practices in Psychological Science · 2020 · 102 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Psychology
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 < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3–9; median total sample = 1,279.5, range = 276–3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (Δ r = .002 or .014, depending on analytic approach). The median effect size for the revised protocols ( r = .05) was similar to that of the RP:P protocols ( r = .04) and the original RP:P replications ( r = .11), and smaller than that of the original studies ( r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00–.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19–.50).
High Replicability of Newly-Discovered Social-behavioral Findings is Achievable
2020 · 56 citations
- Computer Science
- Artificial Intelligence
- Psychology
Failures to replicate evidence of new discoveries have forced scientists to ask whether this unreliability is due to suboptimal implementation of methods or whether presumptively optimal methods are not, in fact, optimal. This paper reports an investigation by four coordinated laboratories of the prospective replicability of 16 novel experimental findings using rigor-enhancing practices: confirmatory tests, large sample sizes, preregistration, and methodological transparency. In contrast to past systematic replication efforts that reported replication rates averaging 50%, replication attempts here produced the expected effects with significance testing (p&lt;.05) in 86% of attempts, slightly exceeding maximum expected replicability based on observed effect sizes and sample sizes. When one lab attempted to replicate an effect discovered by another lab, the effect size in the replications was 97% that of the original study. This high replication rate justifies confidence in rigor enhancing methods to increase the replicability of new discoveries.
The Open Scholarship Survey (OSS)
2020 · 6 citations
Senior authorCorresponding- Political Science
- Political Science
- Law
The OSS is a standard, modular survey to assess open scholarship attitudes, perceptions, and behavior of researchers
Recent grants
Implicit Cognition in STEM Education
NSF · $827k · 2006–2010
Community developing and supporting practices to increase replicability of scientific research
NIH · $1.4M · 2014–2022
NIH · $2.5M · 2009
Frequent coauthors
- 210 shared
Anthony G. Greenwald
- 186 shared
Charles R. Ebersole
University of Virginia
- 179 shared
Jordan Axt
McGill University
- 132 shared
Mahzarin R. Banaji
Harvard University
- 123 shared
David Thomas Mellor
Center for Open Science
- 121 shared
Calvin K. Lai
Washington University in St. Louis
- 93 shared
Yoav Bar‐Anan
Tel Aviv University
- 87 shared
Nicole M. Lindner
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