. This would motivate researchers to either conduct extra analyses themselves or to recruit more analysts as co-authors. Second, journals could invite teams to contribute their own analyses in the form of comments on a recently accepted article.Certainly, large-scale changes in how science is done are possible: expectations surrounding the sharing of data are growing. Medical journals now require that clinical trials be registered at launch for the results to be published.
Yet another approach would be to incorporate multiple analyses into training programs, which would be both useful for the research community and eye-opening for statisticians. Ideally, participating in multiple analyses will be seen as part of being a good science ‘citizen’, and be rewarded through better prospects for hiring and promotion.
Whatever the mix of incentives and formats, the more that multiple analyses efforts are implemented and discussed, the easier they will become. What makes such multi-team efforts work well should be studied and applied to improve and expand the practice. As the scientific community learns how to run multi-team analyses and what can be learnt, acceptance and enthusiasm will grow.
We argue that rejecting the multi-analyst vision would be like Neo opting for the blue pill in the film, and so continuing to dream of a reality that is comforting but false. Scientists and society will be better served by confronting the potential fragility of reported statistical outcomes. It is crucial for researchers and society to have an indication of such fragility from the moment the results are published, especially when these results have real-world ramifications.
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