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Multiversal Methods and Applications

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  • Cantone, Giulio Giacomo
  • Tomaselli, Venera

Abstract

Multiverse analysis is a paradigm for estimation of the uncertainty regarding the veracity of a scientific claim, through a systemic not random sampling of a massive set of specifications of a model, which is the multiverse. Specifications, once fit on a sample, result in statistics. Observation of the variability of result statistics across groups of specifications is considered useful for checking the robustness of the claim or for better understanding its premises. However, the assumptions behind these procedures are not explicit and not always univocal: generally, the proprieties of a multiversal sample hold uniformly only for non-parametric assumptions. A new formal categorisation of the analytical choices in modelling is proposed. It helps to make the assumption of the multiverse more transparent and to check the parametric assumption. These theories are applied to the panel dataset. The analytical process is documented from the design of the hypothesis to the computation of the distribution of estimates for the same generalised linear effect. The analysis highlights the sensitivity of the model to the estimation of fixed covariates in the panel and how these results are so sensitive to this decision to twist the estimates of the linear effect. In the conclusion, the theory of multiversal sampling is related to the debate on how to weigh a multiverse.

Suggested Citation

  • Cantone, Giulio Giacomo & Tomaselli, Venera, 2023. "Multiversal Methods and Applications," MetaArXiv ukvw7, Center for Open Science.
    • Handle: RePEc:osf:metaar:ukvw7
    DOI: 10.31219/osf.io/ukvw7
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    1. Nate Breznau & Eike Mark Rinke & Alexander Wuttke & Hung H. V. Nguyen & Muna Adem & Jule Adriaans & Amalia Alvarez-Benjumea & Henrik K. Andersen & Daniel Auer & Flavio Azevedo & Oke Bahnsen & Dave Bal, 2022. "Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(44), pages 2203150119-, November.
    2. Nate Breznau & Eike Mark Rinke & Alexander Wuttke & Hung H. V. Nguyen & Muna Adem & Jule Adriaans & Amalia Alvarez-Benjumea & Henrik K. Andersen & Daniel Auer & Flavio Azevedo & Oke Bahnsen & Dave Bal, 2022. "Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty," Decision Analysis, INFORMS, vol. 119(44), pages 2203150119-, November.
    3. Agresti, Alan & Caffo, Brian & Ohman-Strickland, Pamela, 2004. "Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 639-653, October.
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