Crowdsourcing hypothesis tests: making transparent how design choices shape research results

Crowdsourcing Hypothesis Tests Collaboration, Leonid Tiokhin (Contributor)

Research output: Contribution to journalArticleAcademicpeer-review

86 Citations (Scopus)


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 two separate large samples (total N > 15,000) were then randomly assigned to complete one 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 four out of five 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 two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while 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.
Original languageEnglish
Pages (from-to)451-479
Number of pages29
JournalPsychological Bulletin
Issue number5
Publication statusPublished - 1 May 2020


  • Conceptual replications
  • Crowdsourcing
  • Forecasting
  • Research robustness
  • Scientific transparency


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