statistical-significance-annotation
Guide for annotating statistical significance (p-value asterisks) on comparison plots. Covers standard notation (ns, *, **, ***, ****), matplotlib bracket+asterisk implementation, and use with seaborn box/violin/bar plots. Use when preparing publication-ready figures with significance markers.
npx skills add BioTender-max/awesome-bio-agent-skills --skill statistical-significance-annotation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Statistical Significance Annotation on Plots ## Overview Statistical significance annotations (asterisk notation) are visual markers placed on comparison plots to indicate the results of hypothesis tests between groups. They consist of brackets connecting two groups and asterisk symbols denoting the p-value range. Proper annotation ensures that the visual claims in a figure match the quantitative evidence, making plots publication-ready and scientifically rigorous. This guide covers the standard conventions, when and how to annotate, and a reusable matplotlib implementation. ## Key Concepts ### Standard Asterisk Notation The widely adopted convention maps p-value ranges to asterisk symbols: | Symbol | P-value Range | Meaning | |--------|--------------|---------| | ns | p > 0.05 | Not significant | | \* | p <= 0.05 | Significant | | \*\* | p <= 0.01 | Highly significant | | \*\*\* | p <= 0.001 | Very highly significant | | \*\*\*\* | p <= 0.0001 | Extremely significant | The conversion function: ```python def pvalue_to_asterisk(p: float) -> str: """Convert a p-value to standard asterisk notation.""" if p <= 0.0001: return "****" elif p <= 0.001: return "***" elif p <= 0.01: return
- Overview
- Key Concepts
- Standard Asterisk Notation
- Adjusted vs Raw P-values
- Comparison Selection
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Step 1: Compute Statistical Tests
- Step 2: Add Bracket Annotations to the Plot
- Step 3: Integrate with Seaborn Plots
- Step 4: Annotating Grouped Bar Plots
- Protocol Guidelines
What does the statistical-significance-annotation skill do?
Guide for annotating statistical significance (p-value asterisks) on comparison plots. Covers standard notation (ns, *, **, ***, ****), matplotlib bracket+asterisk implementation, and use with seaborn box/violin/bar plots. Use when preparing publication-ready figures with significance markers.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill statistical-significance-annotation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 144 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.