tufte-narrative-and-sequence

Auditing a data visualization for disinformation involves checking if all relevant data including disconfirming cases are shown, and verifying that causal variables map correctly to axes. This identifies masking, false scaling, and chartjunk to ensure data integrity.

Dequantification in statistical graphics is the systemic failure of reducing detailed quantitative information into vague visual representations. It obscures data by stripping away precise scales and values, preventing viewers from accurately interpreting process, motion, or change.

Apply Tufte's principles of evidence reasoning to instructional diagrams by ensuring multivariate display and using the smallest effective difference. This framework inverts disinformation tactics into honest, high-resolution information design that accurately depicts process and causality.

Yes, you can review a dashboard for missing scales and causal variable alignment using this audit framework. It evaluates whether visual displays show all relevant data and correctly map causal variables to axes, ensuring the visual narrative does not mislead through omitted data.

The best way to evaluate motion multiples and animations for data integrity is to analyze them for epistemic failures like dequantification and improper causal mapping. This ensures that visual explanations accurately represent process and change without misleading the viewer.

You should not strictly minimize visual elements when doing so causes dequantification or hides disconfirming data. While the smallest effective difference is a guiding principle, it must not compromise data resolution or obscure the causal relationships in technical presentations.