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vis-experiments

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Use when designing or auditing IEEE VIS evaluations, covering how to match evidence to the contribution type (perceptual study, controlled user study, algorithm benchmark, design-study validation, qualitative work), controlled experiment design with power and effect sizes, CVD-safe and perceptually grounded encoding choices, task taxonomies, and provenance so a TVCG reviewer trusts the result.

VIS-Skills/skills/vis-experiments/SKILL.md

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# VIS Experiments

Use this before submission when the evaluation is not yet locked. IEEE VIS reviewers judge whether
the **evidence matches the contribution type** — and visualization has several distinct contribution
types, each with its own evidence standard. The organizing principle is **evaluate the claim you
actually make**: a claim about *perception* needs a controlled study, a claim about *scale* needs a
benchmark, a claim about *real-world usefulness* needs a design-study validation or a deployment.

## Evaluation audit

- **Pick the evaluation to the contribution type**, not by habit (see the table). The classic VIS
  reject is a system paper "evaluated" only by an accuracy number, or a perceptual claim backed only
  by author intuition.
- **Design controlled studies properly:** state hypotheses, a within/between design, a task from a
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