Paper detail

Publication bias and the canonization of false facts

In the process of scientific inquiry, certain claims accumulate enough support to be established as facts. Unfortunately, not every claim accorded the status of fact turns out to be true. In this paper, we model the dynamic process by which claims are canonized as fact through repeated experimental confirmation. The community's confidence in a claim constitutes a Markov process: each successive published result shifts the degree of belief, until sufficient evidence accumulates to accept the claim as fact or to reject it as false. In our model, publication bias --- in which positive results are published preferentially over negative ones --- influences the distribution of published results. We find that when readers do not know the degree of publication bias and thus cannot condition on it, false claims often can be canonized as facts. Unless a sufficient fraction of negative results are published, the scientific process will do a poor job at discriminating false from true claims. This problem is exacerbated when scientists engage in p-hacking, data dredging, and other behaviors that increase the rate at which false positives are published. If negative results become easier to publish as a claim approaches acceptance as a fact, however, true and false claims can be more readily distinguished. To the degree that the model accurately represents current scholarly practice, there will be serious concern about the validity of purported facts in some areas of scientific research.