inferential-statistics
Drawing conclusions about populations from sample data. Covers sampling distributions, confidence intervals, hypothesis testing (z-tests, t-tests, chi-squared tests, ANOVA), p-values, significance levels, power, Type I and Type II errors, effect sizes, and the logic connecting sample statistics to population parameters. Emphasizes the distinction between statistical significance and practical significance. Use when testing hypotheses, constructing confidence intervals, designing studies, or interpreting inferential results.
npx skills add majiayu000/claude-skill-registry --skill inferential-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.
# Inferential Statistics Inferential statistics is the bridge from sample to population. A researcher observes 200 patients and wants to draw conclusions about all patients. A factory tests 50 parts and wants to guarantee the quality of 10,000. The logical machinery that makes this possible -- sampling distributions, confidence intervals, hypothesis tests, and their attendant concepts of error and power -- forms the core of this skill. **Agent affinity:** pearson (chi-squared, test design), gosset (t-tests, small-sample inference), wasserstein (p-value interpretation, communication), george (pedagogy) **Concept IDs:** stat-hypothesis-testing, stat-sampling-bias, stat-descriptive-statistics ## The Logic of Inference ### From sample to population A **parameter** is a fixed but unknown number describing a population (mu, sigma, p). A **statistic** is a number computed from sample data (x-bar, s, p-hat) that estimates the parameter. The key question: how much can a statistic vary from sample to sample? The **sampling distribution** of a statistic describes this variability. The standard deviation of a sampling distribution is called the **standard error (SE)**. ### The sampling distrib
- The Logic of Inference
- From sample to population
- The sampling distribution of the mean
- Confidence Intervals
- Construction
- Interpretation
- Common confidence intervals
- Width and precision
- Hypothesis Testing
- The framework
- Common tests
- P-Values
- Errors and Power
- Type I and Type II errors
What does the inferential-statistics skill do?
Drawing conclusions about populations from sample data. Covers sampling distributions, confidence intervals, hypothesis testing (z-tests, t-tests, chi-squared tests, ANOVA), p-values, significance levels, power, Type I and Type II errors, effect sizes, and the logic connecting sample statistics to population parameters. Emphasizes the distinction between statistical significance and practical significance. Use when testing hypotheses, constructing confidence intervals, designing studies, or interpreting inferential results.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill inferential-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.