Predicting the replicability of social and behavioural science claims in COVID-19 preprints

Marcoci, Alexandru; Wilkinson, David P.; Vercammen, Ans; Wintle, Bonnie C.; Lou Abatayo, Anna; Baskin, Ernest; Berkman, Henk; Buchanan, Erin M.; Capitán, Sara; Capitán, Tabaré; Chan, Ginny; Cheng, Kent Jason G.; Coupé, Tom; Dryhurst, Sarah; Duan, Jianhua; Edlund, John E.; Errington, Timothy M.; Fedor, Anna; Fidler, Fiona; Field, James G.; Fox, Nicholas; Fraser, Hannah; Freeman, Alexandra L.J.; Hanea, Anca; Holzmeister, Felix; Hong, Sanghyun; Huggins, Raquel; Huntington-Klein, Nick; Johannesson, Magnus; Jones, Angela M.; Kapoor, Hansika; Kerr, John; Kline Struhl, Melissa; Kołczyńska, Marta; Liu, Yang; Loomas, Zachary; Luis, Brianna; Méndez, Esteban; Miske, Olivia; Mody, Fallon; Nast, Carolin; Nosek, Brian A.; Simon Parsons, E.; Pfeiffer, Thomas; Robert Reed, W.; Roozenbeek, Jon; Schlyfestone, Alexa R.; Schneider, Claudia R.; Soh, Andrew; Song, Zhongchen; Tagat, Anirudh; Tutor, Melba; Tyner, Andrew H.; Urbanska, Karolina; van der Linden, Sander

Abstract

Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources. We elicited judgements from participants on 100 claims from preprints about an emerging area of research (COVID-19 pandemic) using an interactive structured elicitation protocol, and we conducted 29 new high-powered replications. After interacting with their peers, participant groups with lower task expertise (‘beginners’) updated their estimates and confidence in their judgements significantly more than groups with greater task expertise (‘experienced’). For experienced individuals, the average accuracy was 0.57 (95% CI: [0.53, 0.61]) after interaction, and they correctly classified 61% of claims; beginners’ average accuracy was 0.58 (95% CI: [0.54, 0.62]), correctly classifying 69% of claims. The difference in accuracy between groups was not statistically significant and their judgements on the full set of claims were correlated (r(98) = 0.48, P < 0.001). These results suggest that both beginners and more-experienced participants using a structured process have some ability to make better-than-chance predictions about the reliability of ‘fast science’ under conditions of high uncertainty. However, given the importance of such assessments for making evidence-based critical decisions in a crisis, more research is required to understand who the right experts in forecasting replicability are and how their judgements ought to be elicited.

Citation

Marcoci, A., Wilkinson, D. P., Vercammen, A., Wintle, B. C., Lou Abatayo, A., Baskin, E., Berkman, H., Buchanan, E. M., Capitán, S., Capitán, T., Chan, G., Cheng, K. J. G., Coupé, T., Dryhurst, S., Duan, J., Edlund, J. E., Errington, T. M., Fedor, A., Fidler, F., Field, J. G., …van der Linden, S. (2024). Predicting the replicability of social and behavioural science claims in COVID-19 preprints. Nature Human Behaviour, https://doi.org/10.1038/s41562-024-01961-1

• Journal Article Type: Article
• Acceptance Date: Jul 19, 2024
• Online Publication Date: Dec 20, 2024
• Publication Date: Dec 20, 2024
• Deposit Date: Oct 4, 2024
• Publicly Available Date: Jun 21, 2025
• Journal: Nature Human Behaviour
• Electronic ISSN: 2397-3374
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1038/s41562-024-01961-1
• Public URL: https://nottingham-repository.worktribe.com/output/40289275
• Publisher URL: https://www.nature.com/articles/s41562-024-01961-1