statistical-analysis
Skill2 starsChanged 12 days agoMITView on GitHub
Statistical Analysis. Hypothesis tests, A/B tests and experiments, confidence intervals, effect sizes, power and sample size, multiple comparisons, regression diagnostics, Bayesian alternatives, time series (stationarity, ARIMA, ETS) and survival analysis (Kaplan-Meier, Cox), with causal caution (randomization, difference-in-differences, matching, instrumental variables). Use for statistics, significance testing, t-test, chi-squared, ANOVA, Mann-Whitney, correlation, regression interpretation, experiment readouts, or any causal question.
46 other projects use a version of this.
Adopted by
Most recently taken up by
- BrightGold70/skillstheir own version, first seen Nov 8, 2026
- nisarth/claude-skills-storetheir own version, first seen Nov 8, 2026
- AxlZero21/Skillstheir own version, first seen Nov 8, 2026
- TerryFYL/ai-research-armytheir own version, first seen Nov 8, 2026
- SWEStash/swe-workflow-skillstheir own version, first seen Nov 8, 2026
- DaeSeokSong/oh-my-ouroborostheir own version, first seen Nov 1, 2026
Used with
Projects with it also use evidence-driven-writing, experiment-results-planning and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
Norman-burySkillevidence-driven-writing
Use when writing or revising Introduction, Related Work, background, literature synthesis, or any section where references must drive claims
Also in 3 projects that use this
- Norman-burySkill
experiment-results-planning
Use when designing experiments, result tables, mock planning data, evaluation protocols, or results sections before real data are final
Also in 3 projects that use this
- +10 more from Norman-bury
Other versions46 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 46
- anthropics/knowledge-work-plugins★ 35ksame name
- Norman-bury/research-writing-skill★ 3.4ksame name
- Norman-bury/articlewriting-skill★ 3.4ksame name
- wu-yc/LabClaw★ 1.1ksame name
- LeonChaoX/qinyan-academic-skills★ 910same name
- jaechang-hits/SciAgent-Skills★ 386same name
- qinyan-ai/qinyan-academic-skills★ 80same name
- Barrios88/barrios-skills★ 63same name
- Chanw-research/claude-code-paper-writing★ 42same name
- TashanGKD/tashan-research-skills★ 19same name
36 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository16 other files it gives its agents.
- ai-prediction-explainerSkill
- data-mining-machine-learningSkill
- data-miningSkill
- data-preparationSkill
- data-profilingSkill
- data-storytellingSkill
- feature-engineeringSkill
- hyperparameter-tuningSkill
- ml-quality-guardSkill
- ml-web-applicationSkill
- model-deploymentSkill
- model-evaluationSkill
- model-explainabilitySkill
- model-trainingSkill
- notebook-auditSkill
- problem-framingSkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 2 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 39 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 20
Check independence, normality (Shapiro-Wilk with Q-Q plot, sample-size aware), equal variance (Levene), and sample size, then follow …
- Distinctive: line 35
No peeking. If interim looks are needed, use a pre-registered sequential design (alpha spending).
- Distinctive: line 37
Check sample ratio mismatch (chi-squared on assignment counts) before reading results.
“Common” is measured: kept by at least 6 in 10 of the 39 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Statistical Analysis
Explore first (Tukey), then infer. A bare p-value is a defect.
Before running any test, write
- Question in plain words.
- Estimand: the exact quantity (difference in means, ratio of proportions, hazard ratio).
- Null and alternative, one- or two-sided, and alpha.
- Design: randomized, observational, paired, clustered.
- Minimum effect worth acting on, and the power to detect it.
Choose the test from the data
Check independence, normality (Shapiro-Wilk with Q-Q plot, sample-size aware), equal variance (Levene), and sample size, then follow references/test-selection.md. Default to Welch's t-test over Student's. Use rank or exact tests when assumptions fail.
Report
| Always | Example form |
| Effect size | difference, Cohen's d, odds ratio, Cramer's V |
| Interval | 95 percent CI (analytical or bootstrap) |
| p-value | exact, with the test named |
| Sample sizes | per group |
| Correction | Holm or Benjamini-Hochberg when several tests |
Experiments and A/B tests
- Power analysis before launch (
statsmodels.stats.power). Fix duration and sample size in advance. - No peeking. If interim looks are needed, use a pre-registered sequential design (alpha spending).
- One primary metric; guardrails reported but not used to declare a win.
- Check sample ratio mismatch (chi-squared on assignment counts) before reading results.
- Clustered randomization needs cluster-robust errors.
Causation
Correlation is not causation. A causal claim needs a design: randomization, difference-in-differences (parallel trends), matching or weighting (no unmeasured confounding), instrumental variables (exclusion restriction), regression discontinuity. List the untestable assumptions. Without a design, write "is associated with".
Regression
Diagnostics before reading coefficients: residual plots, VIF, influence (Cook's distance), Breusch-Pagan, Q-Q plot. Use robust (HC3) standard errors when heteroscedastic.
Bayesian alternative
Offer it for small samples or when priors matter: Beta-Binomial for conversion rates, normal models with weakly informative priors; report posterior intervals and the probability the effect exceeds the minimum meaningful effect.
Time series and survival
Stationarity (ADF, KPSS), decomposition, ARIMA or ETS with residual diagnostics, forward-chaining validation; Kaplan-Meier, log-rank, Cox with proportional-hazards checks, censoring explained. references/time-series-survival.md.
Does it work?
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.