Humans often fail, when they deal with Bayesian situations, that is, situations in which Bayes’ formula has to be applied. In our research, we contribute to the question, in which way visualization supports or bounds peoples performance in Bayesian situations. We built upon our former research, in which we showed that a double-tree, a unit square and a 2x2-table are more effective in facilitating Bayesian reasoning than the tree diagram. To investigate the properties of these four visualizations further, we investigate in this paper the solutions of students when dealing with Bayesian situations that deviate from the correct solution. Our sample consists of 542 university students. The students were randomly assigned to four conditions, that is, the different visualizations of statistical information. The students were asked to indicate a fraction as the solution in four Bayesian situations. We documented the numerator and the denominator of the students’ solutions and analysed the data by identifying different strategies. Our results show that people’s strategies are highly dependent from the visualization. A central finding is that the tree diagram hinders to find the correct denominator in a Bayesian situation, and forces to select a wrong numerator represented by a node in the tree diagram. By analyzing the students’ strategies in Bayesian situations in depth, we contribute to the small amount of research that investigates strategies of dealing with Bayesian situations.