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Volume 652 Issue 8108, 2 April 2026

Repeat performance

Three key factors help to show whether a study’s results can be trusted: reproducibility (the same conclusion can be derived using the study’s methods and data), replication (the study’s conclusions can be achieved using independent data) and analytical robustness (reanalysis of the data by multiple teams can generate the same conclusions). In this week’s special issue, four papers present multi-team collaborations testing these criteria in the social and behavioural sciences. Across two papers, Brian Nosek and colleagues examine reproducibility and replication. To assess reproducibility, the researchers looked at 145 papers and found they could exactly reproduce only 54%, although around 75% were approximately reproducible. To probe replication, the team retested 274 published claims from 164 papers; again, only some 55% showed the same patterns as the original results. In a third paper, Balazs Aczel and co-workers tested analytical robustness by having multiple teams reanalyse 100 studies. Only a fraction reproduced the original findings closely — although most did arrive at similar overall conclusions. And in a fourth paper, Abel Brodeur and colleagues focused on economics and political science with a large-scale assessment of 110 papers, finding that 85% of the claims were reproducible computationally. Overall, these papers highlight that approaches such as transparent workflows could help bolster confidence in results from the social and behavioural sciences.

Cover image: Ibrahim Rayintakath.

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