What is it about?
To better explore our research data, a double approach to data analysis may give us the best of two worlds: A significance test allows us to be wary of errors; Bayes factors allows us to explore the weight of alternative explanations.
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Why is it important?
A recent proposal by Benjamin et al. (2017) calls for the use of a more stringent level of significance (p < 0.005, instead of p < 0.05) for claiming proof of evidence for novel results Unfortunately, statistical significance and substantive evidence imply different philosophical approaches, and the statistical tools derived under each approach are not necessarily compatible. A large number of authors have criticized Benjamin et al.'s proposal because of the mix-up, which could have been easily avoided by recommending a double-approach to data analysis (e.g., via using a data analysis software such as JASP).
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This page is a summary of: Retract p < 0.005 and propose using JASP, instead, F1000Research, December 2017, Faculty of 1000, Ltd., DOI: 10.12688/f1000research.13389.1.
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