Summary Statistics
Compute and interpret descriptive statistics: central tendency, dispersion, shape, and confidence intervals. Goes beyond pandas .describe() with statistical interpretation and contextual meaning. Use when you need a thorough statistical summary of your data.
Install
$ agentstack add skill-ericwang915-data-scientist-skills-summary-statistics ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-ericwang915-data-scientist-skills-summary-statistics) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Summary Statistics
Purpose
Provide a comprehensive statistical summary with interpretation — not just numbers, but what they mean for your analysis.
How It Works
Step 1: Central Tendency
- Mean, median, mode — and which is most appropriate for this distribution
- Trimmed mean (robust to outliers)
- Weighted mean (if weights are provided)
Step 2: Dispersion
- Standard deviation, variance, coefficient of variation
- IQR, range, MAD (Median Absolute Deviation)
- Confidence intervals for the mean (95% CI)
Step 3: Shape
- Skewness (with interpretation: symmetric, right-skewed, left-skewed)
- Kurtosis (with interpretation: leptokurtic, mesokurtic, platykurtic)
- Normality tests: Shapiro-Wilk, D'Agostino-Pearson, Anderson-Darling
Step 4: Percentiles & Distribution
- Full percentile breakdown (1, 5, 10, 25, 50, 75, 90, 95, 99)
- Five-number summary with box plot
- Distribution fitting: identify the best-fitting distribution
Step 5: Group Comparisons (if applicable)
- Statistics by category or time period
- Effect sizes between groups (Cohen's d, Cliff's delta)
- Statistical significance of group differences
Usage Examples
"Calculate summary statistics for the conversion_rate column,
grouped by marketing channel""Is this revenue data normally distributed? Which summary statistic
should I report — mean or median?"Output Format
- Statistics Table: Full descriptive statistics with 95% CIs
- Interpretation: Plain-language explanation of what the numbers mean
- Distribution Assessment: Normality test results with recommendation
- Python Code: Reproducible computation script
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: ericwang915
- Source: ericwang915/data-scientist-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.