How the level of inference in visualizations influences participants’ performance in Bayesian reasoning
DOI:
https://doi.org/10.52041/iase25.123Abstract
We conducted a study with 2,400 participants that had to solve six Bayesian reasoning tasks in one out of eight different visualization types (no visualization, regular 2×2 table, graphical 2×2 table, unit square, implicit tree diagram, explicit tree diagram, double tree, net diagram) in a probability, proportion or frequency format. The aim of the study was to investigate whether the levels of inference have an influence on the participants’ performance in the tasks. The level of inference is characterized by the number of mental steps that are needed to arrive at the correct solution, which vary between the visualization types. The results show that the levels of inference indeed influence performance. This can be used to teach students to adaptively and flexibly use probabilistic visualizations for different types of tasks.References
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