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Observing Many Researchers using the Same Data and Hypothesis Reveals a Hidden Universe of Data Analysis

Author

Listed:
  • Breznau, Nate

    (University of Bremen)

  • Rinke, Eike Mark

    (University of Leeds)

  • Wuttke, Alexander

    (University of Mannheim)

  • Adem, Muna
  • Adriaans, Jule
  • Alvarez-Benjumea, Amalia

    (Max Planck Institute for Research on collective goods)

  • Andersen, Henrik Kenneth
  • Auer, Daniel
  • Azevedo, Flavio

    (Cologne University)

  • Bahnsen, Oke

Abstract

Findings from 162 researchers in 73 teams testing the same hypothesis with the same data reveal a universe of unique analytical possibilities leading to a broad range of results and conclusions. Surprisingly, the outcome variance mostly cannot be explained by variations in researchers’ modeling decisions or prior beliefs. Each of the 1,261 test models submitted by the teams was ultimately a unique combination of data-analytical steps. Because the noise generated in this crowdsourced research mostly cannot be explained using myriad meta-analytic methods, we conclude that idiosyncratic researcher variability is a threat to the reliability of scientific findings. This highlights the complexity and ambiguity inherent in the scientific data analysis process that needs to be taken into account in future efforts to assess and improve the credibility of scientific work.

Suggested Citation

  • Breznau, Nate & Rinke, Eike Mark & Wuttke, Alexander & Adem, Muna & Adriaans, Jule & Alvarez-Benjumea, Amalia & Andersen, Henrik Kenneth & Auer, Daniel & Azevedo, Flavio & Bahnsen, Oke, 2021. "Observing Many Researchers using the Same Data and Hypothesis Reveals a Hidden Universe of Data Analysis," MetaArXiv cd5j9, Center for Open Science.
    • Handle: RePEc:osf:metaar:cd5j9
    DOI: 10.31219/osf.io/cd5j9
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    References listed on IDEAS

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    Cited by:

    1. Goldman, Daniel S, 2021. "A Reply to Breznau, Rinke, and Wuttke on the Discordant Outcomes of Hypothesis Testing," MetaArXiv qs5gm, Center for Open Science.
    2. Ruohuang Jiao & Wojtek Przepiorka & Vincent Buskens, 2022. "Moderators of reputation effects in peer-to-peer online markets: a meta-analytic model selection approach," Journal of Computational Social Science, Springer, vol. 5(1), pages 1041-1067, May.

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