Statistilises

Statistilises is a multidisciplinary approach that examines the presentation, interpretation, and ethics of statistical information. It sits at the intersection of statistics, data visualization, rhetoric, and philosophy of science. The field investigates how charts, tables, labels, and narratives influence understanding, judgment, and decision-making.

The term has emerged in scholarly discussions and design curricula in the late 2010s as analysts noted that even accurate data can be misinterpreted through design choices. There is no single governing body or standardized curriculum; practice varies by domain, with researchers and practitioners drawing on statistics, cognitive science, semiotics, and design theory.

Core concerns include uncertainty visualization, responsible simplification, comparability, accessibility, and transparency. Methods include critique of visualization choices, analysis of rhetorical framing, development of guidelines for effective communication of p-values, confidence intervals, effect sizes, and replication results, and evaluation studies on reader comprehension and bias.

Applications appear in journalism, public health communication, policy briefing, and educational materials. Examples include visualization guidelines that label data quality, annotate limitations, and present multiple scenarios, or curricula that teach statistilises principles to improve media literacy and critical thinking about numbers.

Critics caution against overemphasizing aesthetics at the expense of accuracy, or using vague rhetorical concerns to justify misleading representations. Proponents argue that structured attention to presentation improves reproducibility and trust, provided uncertainty and bias are properly disclosed. In practice, statistilises fosters dialogue among statisticians, designers, educators, and communicators to enhance clear and responsible data storytelling.