![]() ![]() ![]() ![]() The red path shows one possible trajectory, in which a positive experiment is followed by a negative, then two positives, then a negative, etc., ultimately becoming canonized as fact when it reaches the upper boundary. The black horizontal lines show the evidentiary standards ( τ 0 and τ 1). At green nodes, it is canonized as fact, and at blue nodes, it is rejected as false. At yellow nodes, the status of the claim is as yet undecided. Each subsequent experiment either supports the claim, moving to the next node up and right, or contradicts the claim, moving to the next node down and right. The process begins at the single point at far left with an initial belief q 0. In panel A, the horizontal axis indicates the number of experiments published and the vertical axis reflects the observer’s belief, quantified as the probability that the claim is true. To the degree that the model reflects the real world, there may be serious concerns about the validity of purported facts in some disciplines.įalse positive hypothesis testing none publication bias replication crisis. Should negative results become easier to publish as a claim approaches acceptance as a fact, however, true and false claims would be more readily distinguished. Data-dredging, p-hacking, and similar behaviors exacerbate the problem. We find that unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact. Publication bias in favor of positive findings influences the distribution of published results. ![]() We model the community's confidence in a claim as a Markov process with successive published results shifting the degree of belief. Does this imply that many scientific facts are false as well? To find out, we explore the process by which a claim becomes fact. Science is facing a "replication crisis" in which many experimental findings cannot be replicated and are likely to be false. ![]()
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