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More alarming is the general paucity in the literature of negative data. In some fields, almost all published studies show formally significant results so that statistical significance no longer appears discriminating [15,16]. Discovering selective reporting is not easy, but the implications are dire, as in the “hidden” results for antidepressant trials [17]: in a recent paper, it was shown that while almost all trials with “positive” results on antidepressants had been published, trials with “negative” results submitted to the US Food and Drug Administration, with few exceptions, remained either unpublished or were published with the results presented so that they would appear “positive” [17]. Negative or contradictory data may be discussed at conferences or among colleagues, but surface more publicly only when dominant paradigms are replaced. Sometimes, negative data do appear in refutation of prominent claims. In the “Proteus phenomenon”, an extreme result reported in the first published study is followed by an extreme opposite result; this sequence may cast doubt on the significance, meaning, or validity of any of the results [18]. Several factors may predict irreproducibility (small effects, small studies, “hot” fields, strong interests, large databases, flexible statistics) [19], but claiming that a specific study is wrong is a difficult, charged decision.

In the basic biological sciences, statistical considerations are secondary or nonexistent, results entirely unpredicted by hypotheses are celebrated, and there are few formal rules for reproducibility [20,21]. A signalling benefit from the market—good scientists being identified by their positive results—may be more powerful in the basic biological sciences than in clinical research, where the consequences of incorrect assessment of positive results are more dire. As with clinical research, prominent claims sometimes disappear over time [21]. If a posteriori considerations are met sceptically in clinical research, in basic biology they dominate. Negative data are not necessarily different than positive results as related to considerations of experimental design, execution, or importance. Much data are never formally refuted in print, but most promising preclinical work eventually fails to translate to clinical benefit [22]. Worse, in the course of ongoing experimentation, apparently negative studies are abandoned prematurely as wasteful.