Students will investigate how real people interpret statistics about current events, conduct firsthand interviews, and create evidence-based empathy maps that reveal misunderstandings and needs related to data distributions and normal modeling.

Students will synthesize insights from their real-user interviews to define a clear, evidence-based problem statement about how people interpret data distributions in real-world events. They will identify patterns across users, analyze how statistical misunderstandings connect to distribution shape and modeling choices, and craft a grounded How Might We statement that will guide solution ideation.

Students will generate, expand, and refine multiple solution concepts for helping community members interpret real-world data distributions accurately. Teams will move from high-volume brainstorming to structured idea expansion and evidence-based convergence, producing annotated concept sketches and a user-interaction storyboard that connects statistical choices (mean, median, standard deviation, normal modeling) directly to documented user needs.

Students will build, test, and refine low- and medium-fidelity prototypes of their interactive data distribution exhibits, using real user feedback and statistical evidence to improve clarity, accuracy, and modeling decisions before final presentation.

Students will validate their interactive data distribution exhibits with a new user, refine their statistical justifications and visualizations, and present their design journey to peers, teachers, administrators, and invited community partners. They will explicitly connect user feedback to modeling decisions about normal distributions and reflect on how understanding data distributions shapes interpretation of real-world events.