descriptive-statistics
Summarizing and visualizing data through measures of center, spread, shape, and position. Covers mean, median, mode, range, IQR, variance, standard deviation, percentiles, z-scores, five-number summaries, and graphical displays (histograms, box plots, stem-and-leaf, dot plots, scatter plots). Emphasizes choosing the right summary for the data's shape and scale. Use when summarizing datasets, choosing visualizations, computing summary statistics, or interpreting distributions.
npx skills add majiayu000/claude-skill-registry --skill descriptive-statistics --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.
# Descriptive Statistics Descriptive statistics distills raw data into interpretable summaries. Before any inference, modeling, or prediction, a dataset must be described: its center, its spread, its shape, and its notable features. This skill covers the full toolkit of numerical summaries and graphical displays that turn observations into understanding. **Agent affinity:** pearson (measures of association), gosset (small-sample summaries), george (pedagogy) **Concept IDs:** stat-descriptive-statistics, stat-data-visualization ## Measures of Center The center of a distribution answers "where is the typical value?" | Measure | Definition | Best for | Sensitive to | |---|---|---|---| | Mean | Sum of values divided by count | Symmetric distributions | Outliers, skew | | Median | Middle value when sorted | Skewed distributions, ordinal data | Nothing -- robust | | Mode | Most frequent value | Categorical data, multimodal distributions | Ties, bin width | | Trimmed mean | Mean after removing k% from each tail | Distributions with mild outliers | Choice of trim percentage | **Decision rule.** Use the mean when the distribution is roughly symmetric with no extreme outliers. Use the median
- Measures of Center
- Measures of Spread
- Shape
- Symmetry and skewness
- Kurtosis
- Modality
- Position Measures
- Percentiles and quartiles
- Z-scores
- Graphical Displays
- Choosing the right graph
- Histogram construction
- Box plot construction
- Bivariate Description
What does the descriptive-statistics skill do?
Summarizing and visualizing data through measures of center, spread, shape, and position. Covers mean, median, mode, range, IQR, variance, standard deviation, percentiles, z-scores, five-number summaries, and graphical displays (histograms, box plots, stem-and-leaf, dot plots, scatter plots). Emphasizes choosing the right summary for the data's shape and scale. Use when summarizing datasets, choosing visualizations, computing summary statistics, or interpreting distributions.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill descriptive-statistics --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 majiayu000/claude-skill-registry, a repository with 534 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.