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If journals embraced conditional equivalence testing, would research be better?

University of British Columbia

We consider the reliability of published science: the probability that scientific claims put forth are true. Low reliability within many scientific fields is of major concern for researchers, scientific journals and the public at large. In the first part of this thesis, we introduce a publication policy that incorporates ''conditional equivalence testing'' (CET), a two-stage testing scheme in which standard null-hypothesis significance testing is followed, if the null hypothesis is not rejected, by testing for equivalence. The idea of CET has the potential to address recent concerns about reproducibility and the limited publication of null results. We detail the implementation of CET, investigate similarities with a Bayesian testing scheme, and outline the basis for how a scientific journal could proceed to reduce publication bias while remaining relevant. In the second part of this thesis, we consider proposals to adopt measures of ''greater statistical stringency,'' including suggestions to require larger sample sizes and to lower the highly criticized ''p<0.05'' significance threshold. While pros and cons are vigorously debated, there has been little to no modeling of how adopting these measures might affect what type of science is published. We develop a novel model that, given current incentives to publish, predicts a researcher's most rational use of resources in terms of the number of studies to undertake, the statistical power to devote to each study, and the desirable pre-study odds to pursue. Using this model, we investigate the merits of adopting measures of ''greater statistical stringency'' with the goal of informing the ongoing debate. We also use this model to investigate the merits of alternative publication policies, including the registered reports policy and our novel CET publication policy.

Text

2019-07-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/70873

Statistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/