4.8 Article

Science Forum: Consensus-based guidance for conducting and reporting multi-analyst studies

期刊

ELIFE
卷 10, 期 -, 页码 -

出版社

eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.72185

关键词

multi-analyst; metascience; statistical practice; science forum; expert consensus; analytical variability; None

类别

资金

  1. Netherlands Organisation for Scientific Research [406-17-568]
  2. Natural Sciences and Engineering Research Council of Canada [BP-546283-2020]
  3. Fonds de Recherche du Quebec - Nature et Technologies [290978]
  4. European Research Council [726361, 681466, 640638]
  5. VIDI fellowship organisation [016.Vidi.188.001]
  6. VENI fellowship grant [Veni 191G.037]
  7. National Science Foundation [1760052]
  8. Weizmann Institute of Science Israel National Postdoctoral Award Program for Advancing Women in Science
  9. John Templeton Foundation
  10. Templeton World Charity Foundation
  11. Templeton Religion Trust
  12. Arnold Ventures
  13. Institut Europeen d'Administration des Affaires
  14. Div Of Information & Intelligent Systems
  15. Direct For Computer & Info Scie & Enginr [1760052] Funding Source: National Science Foundation
  16. European Research Council (ERC) [681466] Funding Source: European Research Council (ERC)

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This article discusses how employing multiple analysts can improve the robustness of data analysis results and conclusions, and presents consensus-based guidance for conducting such studies.
Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.

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