AI Article Synopsis

  • Many-analyst studies investigate how well different analysis teams can interpret the same dataset and how robust their conclusions are against alternative methods.
  • Typically, these studies only report one outcome measure, like effect size, making it hard to grasp the full impact of different analysis choices.
  • To address this, researchers created the Subjective Evidence Evaluation Survey (SEES) using feedback from experts, helping to evaluate the quality of research design and evidence strength, ultimately offering a deeper understanding of analysis outcomes.

Article Abstract

Many-analysts studies explore how well an empirical claim withstands plausible alternative analyses of the same dataset by multiple, independent analysis teams. Conclusions from these studies typically rely on a single outcome metric (e.g. effect size) provided by each analysis team. Although informative about the range of plausible effects in a dataset, a single effect size from each team does not provide a complete, nuanced understanding of how analysis choices are related to the outcome. We used the Delphi consensus technique with input from 37 experts to develop an 18-item subjective evidence evaluation survey (SEES) to evaluate how each analysis team views the methodological appropriateness of the research design and the strength of evidence for the hypothesis. We illustrate the usefulness of the SEES in providing richer evidence assessment with pilot data from a previous many-analysts study.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11265885PMC
http://dx.doi.org/10.1098/rsos.240125DOI Listing

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