Researchers commonly make dichotomous claims based on continuous test statistics. Many have branded the practice as misuse of statistics, and criticize scientists for suffering from “dichotomania”. However, the role dichotomous claims play in science is not primarily a statistical one, but an epistemological and pragmatic one. The epistemological function of dichotomous claims consists in transforming data into factual statements that can falsify a universal statement. This transformation requires pre-specified methodological decision procedures such as statistical hypothesis testing (e.g., Neyman-Pearson tests). From the perspective of methodological falsificationism these decision procedures are necessary, as probabilistic statements (e.g. continuous test statistics) cannot function as falsifiers of substantive hypotheses. However, they are not sufficient since for dichotomous claims to have any implication regarding theoretical claims about phenomena, there should be a valid derivation chain linking theoretical, experimental and data models. The pragmatic function of dichotomous claims is facilitating scrutiny and criticism among peers by generating contestable statements, a process referred to by Popper as 'conjectures and refutations', through which we can determine which theories withstand scrutiny the best. Abandoning dichotomous claims to combat the misuse of statistics would not improve scientific inferences but will sacrifice these crucial epistemic and pragmatic functions.