Overcoming negative (positive) publication bias
F1000 Research starts initiative to overcome ‘positive publication bias’ (aka ‘negative publication bias)’. Until end of August publication fees are waived for submission of Null results.
Only data that are available via publications—and, to a certain extent, via presentations at conferences—can contribute to progress in the life sciences. However, it has long been known that a strong publication bias exists, in particular against the publication of data that do not reproduce previously published material or that refute the investigators’ initial hypothesis. The latter type of contradictory evidence is commonly known as ‘negative data.’ This slightly derogatory term reflects the bias against studies in which investigators were unable to reject their null hypothesis (H0), a tool of frequentist statistics that states that there is no difference between experimental groups.
Researchers are well aware of this bias, as journals are usually not keen to publish the nonexistence of a phenomenon or treatment effect. They know that editors have little interest in publishing data that refute, or do not reproduce, previously published work—with the exception of spectacular cases that guarantee the attention of the scientific community, as well as garner extra citations (Ioannidis and Trikalinos, 2005). The authors of negative results are required to provide evidence for failure to reject the null hypothesis under numerous conditions (e.g., dosages, assays, outcome parameters, additional species or cell types), whereas a positive result would be considered worthwhile under any of these conditions . Indeed, there is a dilemma: one can never prove the absence of an effect, because, as Altman and Bland (1995) remind us, ‘absence of evidence is not evidence of absence’.
Several journals have already opened their pages to ‘negative’ results. For example, the Journal of Cerebral Blood Flow and Metabolism: Fighting publication bias: introducing the Negative Results section publishes such studies as a one-page summary (maximum 500 words, two figures) in the print edition of the journal, and the accompanying full paper online.