This week's issue of Nature has a good article by Monya Baker on a wide-ranging survey of scientists about reproducible research, and a related editorial. DTLR is most encouraged by the final table in Baker's article, the ratings of factors most likely to improve reproducibility. "More robust experimental design" received the most combined "likely" and "very likely" ratings. I think that this is the right answer. Also highly ranked were "better mentoring/supervision" and "better understanding of statistics". This latter one is a tough call, as statisticians themselves seem not to have reached a consensus on how to move forward, as evidenced in the extensive Discussion items published along with the American Statistical Association's Statement on Statistical Significance and P-values, posted in early March.
DTLR expresses thanks to Nature for keeping the drums beating on reproducible research. The issue is very visible right now, and the community should strike while the iron is hot, in terms of reforming the infrastructure of our community (laboratory practices, publication standards, and incentives for grant funding, promotion, and tenure). Mis-aligned incentives are ultimately the cause of non-reproducibility, though methodological issues (poor study design and execution, inappropriate use of statistical methods, etc.) are key enablers.
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