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Description: Sparse (and occasionally contradictory) evidence exists on the impact of domain on probabilistic updating, some of which suggest that Bayesian word problems with medical content may be especially challenging. The present research aims to address this gap in knowledge through three pre-registered online studies, which involved a total of 2,238 participants. Bayesian word problems were matched in terms of all relevant values and (as much as possible) wording and were related to one of three domains: medical, daily-life, and abstract. Medical and daily-life problems presented realistic content and plausible numerical information, while abstract problems contained explicit imaginary elements. Studies 1 and 2 utilized the same set of problems, but different response elicitation methods (i.e., an open-ended and a multiple-choice question, respectively). Study 3 involved a larger number of participants and a smaller set of problems to more thoroughly investigate the magnitude of differences between the domains. Despite a generally very low accuracy rate (17.2%, 17.4%, and 14.3%, in Study 1, 2 and 3, respectively), a small but significant difference between domains was observed: participants’ accuracy did not differ between medical and daily-life problems, while it was significantly higher in corresponding abstract problems. These results suggest that medical problems are not inherently more difficult to solve, but rather that participants’ performance is improved with abstract problems for which participants cannot draw from their background knowledge.