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statistical-and-uncertainty-visualization

Design charts and dashboards that show data distributions, uncertainty, missing values, and sample sizes clearly. Choose visual forms that preserve meaningful variation and explain where averages, aggregation, or interval choices could mislead readers.

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原文查看原文说明

Design statistically honest and uncertainty-aware visualizations. Use when the user needs help showing distributions, intervals, confidence, missingness, sampling effects, or analytical rigor in charts and dashboards.

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适用场景

  • Choosing a chart to show a data distribution
  • Presenting confidence intervals clearly
  • Revealing variation hidden by dashboard averages
  • Showing missing values and sample sizes

包含的文件(6)

  • SKILL.md2.0 KB
  • agents/openai.yaml202 B
  • references/distribution-and-summary-choices.md930 B
  • references/experimental-and-analytical-pitfalls.md879 B
  • references/missingness-and-confidence.md816 B
  • references/uncertainty-encodings.md844 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Statistical and Uncertainty Visualization

Overview

Use this skill when the risk is analytical distortion rather than rendering difficulty. This skill focuses on distributions, intervals, uncertainty, missingness, aggregation effects, and common statistical storytelling failures.

Default assumption: if a claim depends on variability, estimation, sampling, or model uncertainty, the visualization should show that explicitly.

Working Pattern

  1. Identify whether the viewer needs exact values, distributions, intervals, or model-derived estimates.
  2. Choose encodings that show spread, uncertainty, missingness, or sample size honestly.
  3. Avoid summarizing away the variation that matters to the decision.
  4. Pair concise explanations with the view when the uncertainty concept is nontrivial.

Output Expectations

  • Name the statistical question, not just the chart type.
  • Explain why the chosen encoding is more truthful than the tempting alternative.
  • Call out when aggregation, smoothing, or interval choice can mislead.

References

  • Shared theory:
    • ../../references/foundations/task-abstraction-and-chart-selection.md
    • ../../references/foundations/perception-color-and-encoding.md
  • Skill references:
    • ./references/distribution-and-summary-choices.md
    • ./references/uncertainty-encodings.md
    • ./references/experimental-and-analytical-pitfalls.md
    • ./references/missingness-and-confidence.md

Representative Prompts

  • "What chart should I use to show uncertainty here?"
  • "Should this be a histogram, box plot, violin plot, or density plot?"
  • "How do I show confidence intervals without misleading people?"
  • "This dashboard hides variability. How should I fix it?"
  • "Help me visualize missing data and sample size honestly."

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