The impact of misinterpreted evidence on scientific inquiry

Abstract Scientists often base their reasoning on flawed data, overlook confounding factors, or employ incorrect diagnostic methods. Yet, current simulation models of scientific inquiry typically assume that the data underlying scientific assessments is gathered through adequate methods, such as appropriate experimental procedures. In this paper, we introduce a computational model in which scientists can produce and update their beliefs on spurious evidence and ignore properly gathered evidence. In this way, we investigate how misinterpreting evidence impacts the success of the collective inquiry. We find that misinterpretation has two central effects: diversification and distortion. Diversification fosters diverse evidential bases, promoting extensive exploration, while distortion leads to incorrect beliefs, swaying agents towards sub-optimal actions. Using this insight, we show under which conditions misinterpretation benefits a community and what mechanisms allow scientists to profit from it. Moreover, we challenge the predominantly positive view of transient diversity—where scientific communities engage in parallel exploration of rival theories. We demonstrate that under severe distortion, transient diversity intrinsically increases the chance of the community converging on a wrong theory.