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Using prediction markets to estimate the reproducibility of scientific research

Dreber, Anna, Thomas Pfeiffer, Johan Almenberg, Siri Isaksson, Brad Wilson, Yiling Chen, Brian A. Nosek, Magnus Johannesson. Using prediction markets to estimate the reproducibility of scientific research. PNAS 112, no. 50: 15343-15347. doi: 10.1073/pnas.1516179112 [Peer Reviewed Journal]

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  • Title:
    Using prediction markets to estimate the reproducibility of scientific research
  • Author: Dreber-Almenberg, Anna ; Pfeiffer, Thomas ; Almenberg, Johan ; Isaksson, Siri ; Wilson, Brad ; Chen, Yiling ; Nosek, Brian A. ; Johannesson, Magnus
  • Found In: Dreber, Anna, Thomas Pfeiffer, Johan Almenberg, Siri Isaksson, Brad Wilson, Yiling Chen, Brian A. Nosek, Magnus Johannesson. Using prediction markets to estimate the reproducibility of scientific research. PNAS 112, no. 50: 15343-15347. doi: 10.1073/pnas.1516179112 [Peer Reviewed Journal]
  • Subjects: Reproducibility ; Replications ; Prediction Markets
  • Language: English
  • Description: Concerns about a lack of reproducibility of statistically significant results have recently been raised in many fields, and it has been argued that this lack comes at substantial economic costs. We here report the results from prediction markets set up to quantify the reproducibility of 44 studies published in prominent psychology journals and replicated in the Reproducibility Project: Psychology. The prediction markets predict the outcomes of the replications well and outperform a survey of market participants’ individual forecasts. This shows that prediction markets are a promising tool for assessing the reproducibility of published scientific results. The prediction markets also allow us to estimate probabilities for the hypotheses being true at different testing stages, which provides valuable information regarding the temporal dynamics of scientific discovery. We find that the hypotheses being tested in psychology typically have low prior probabilities of being true (median, 9%) and that a “statistically significant” finding needs to be confirmed in a well-powered replication to have a high probability of being true. We argue that prediction markets could be used to obtain speedy information about reproducibility at low cost and could potentially even be used to determine which studies to replicate to optimally allocate limited resources into replications.
  • Identifier: ISSN: 0027-8424 ; DOI: 10.1073/pnas.1516179112

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