This course delves into the theories of inference for visual analysis, focusing on optimizing visualizations for pattern recognition, understanding distribution and uncertainty, evaluating effect size judgments, Bayesian cognition for belief updating, supporting causal inference, and facilitating model checks. Students will acquire skills such as sensitivity to visual distances between means, exploring the mean difference heuristic, and employing graphical inference as a Bayesian model check. Through lectures and discussions, participants will engage with the material to enhance their understanding of visual analysis, data visualization, and statistical inference. Targeted towards individuals interested in leveraging visual analysis techniques, this course offers valuable insights into interpreting data visually and making informed decisions based on statistical inference principles.