The apparent difficulty people have with making Bayesian inferences has been researched heavily over the past 25 years, with conflicting explanations regarding the causes of and the cures for this inadequacy. Some researchers have improved Bayesian reasoning by representing the problem visually, but usually as a tool to teach Bayesian reasoning skills. This research examines facilitating reasoning performance in naïve Bayesian subjects without attempting to teach Bayesian reasoning skills. This approach is more relevant for everyday decision support situations where subjects do not or need not possess knowledge of Bayes theorem (naïve subjects). Several different visual representations (VRs) will be examined to determine which visualization technique generates the best decision performance. For this specific problem, certain visualization representations (VRs) may reveal the problem structure better than others, improving decision making, regardless of the whether number is represented as a natural frequency or a probability. VRs should be stable with regard to different base rates and reference class sizes. Using dual processing theories of cognition, this research will explain other aspects of this judgment task, including how users create and choose their strategies in solving this task and why subjects may have low levels of confidence in their results yet exhibit high task performance. Hopefully this research will help paint a clearer picture of the best ways for decision support systems to represent information in Bayesian inference tasks to naïve subjects and how VRs can enhance naïve subject performance in a variety of judgment and decision making tasks.