PREreviews de “Explanation implies causation?”

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Explanation implies causation?

por Leslie Myint, Jeffrey T. Leek y Leah R. Jager

Publicado: 13 de noviembre de 2017
Servidor: bioRxiv
DOI: 10.1101/218784

Resumen

Most researchers do not deliberately claim causal results in an observational study. But do we lead our readers to draw a causal conclusion unintentionally by explaining why significant correlations and relationships may exist? Here we perform a randomized study in a data analysis massive online open course to test the hypothesis that explaining an analysis will lead readers to interpret an inferential analysis as causal. We show that adding an explanation to the description of an inferential analysis leads to a 15.2% increase in readers interpreting the analysis as causal (95% CI 12.8% - 17.5%). We then replicate this finding in a second large scale massive online open course. Nearly every scientific study, regardless of the study design, includes explanation for observed effects. Our results suggest that these explanations may be misleading to the audience of these data analyses.

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PREreview de Chris Hartgerink y colaboradores/as

Escrito por Chris Hartgerink, Laura Kunst y Linda H. Jütten

PREreview: Explanation implies causation? (Myint, Leek, & Jager, 2017)
A preprint review of "Explanation implies causation?" by Myint, Leek, & Jager. Posted on bioRxiv, November 13 2017. doi: 10.1101/218784Thank you for posting this preprint on bioRxiv! We had fun reading and discussing it. In…
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PREreviews de Explanation implies causation?

1 PREreview

PREreview: Explanation implies causation? (Myint, Leek, & Jager, 2017)