claude

applied-statistics

Applied-statistics team — a specialist agent (applied-statistician) that answers 'is this difference/trend statistically REAL?' for SMB consulting: which test to use (decision-tree-driven), A/B-test and experiment design (power/MDE, primary + guardrail metrics, pre-registered analysis plans), regression and time-series forecasting review, a causal-inference primer, and statistical QA of dashboard metrics. skills, a knowledge bank (a Mermaid test-selection decision tree, a 9-item pitfalls guardrail, experiment-design best practice, 2026 tooling tiers, a causal-inference primer), templates, and an advisory anti-pattern hook. Python-first (scipy.stats / statsmodels / pingouin Tier 1; PyMC / bambi / linearmodels Tier 2). Seams cleanly with data-platform: data-platform answers 'is this number correct?'; this plugin answers 'is it real?'. Method-before-library; always report effect size + CI. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.6.3MITupdated today
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Plugin applied-statistics from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.

What it installs

Skills (5)

  • choose-statistical-test

    Pick the right hypothesis test for a described scenario by traversing the test-selection decision tree (data type → #groups → paired? → assumption gate → test), then return the recommended test, its assumption checks, its nonparametric fallback, and a ≤10-line runnable snippet. Reach for this when t

  • experiment-analysis

    Analyze a completed A/B test or experiment defensibly — check it against the pre-registered plan, run the primary-metric test, report effect size + CI (not just p), check guardrail metrics, apply a multiple-comparison correction across metrics/segments, and screen for the peeking/p-hacking pitfalls

  • power-and-sample-size

    Compute or advise the sample size an experiment needs BEFORE it launches — from α (0.05), power (0.80), and a minimum detectable effect (MDE) — or compute the power/MDE a fixed sample can achieve. Prevents the underpowered-study pitfall and is the prerequisite to any A/B test. Returns the n, the ass

  • regression-and-forecasting-review

    Review or design a regression model or a time-series forecast so it's defensible — pick the model family (OLS / logistic / Poisson GLM; ARIMA / SARIMAX / ETS), check the assumptions that matter for that family, report honest prediction/confidence intervals, and screen for overfitting, data leakage,

  • statistical-qa-of-metrics

    Decide whether a dashboard metric movement, comparison, or trend is signal or noise — and annotate it honestly (significance, confidence interval, "not enough data yet"). The interop seam with data-platform — invoked by `data-platform/dashboard-builder` when a widget shows a comparison/trend that ne

Slash commands (9)

  • /applied-statistics:analyze-dashboard-metric-movement

    Decide whether a dashboard metric movement is real or noise — quantify the metric's baseline variation, test the move against it, require more than two points for a trend, and emit the uncertainty band the widget should carry.

  • /applied-statistics:assess-causal-claim

    Gut-check a 'does X cause Y?' claim: run the correlation-vs-causation gate, pick a causal identification strategy matched to the natural variation, and state its load-bearing assumption out loud.

  • /applied-statistics:check-test-assumptions

    Run the assumption gate on a candidate test before quoting any result, and drop to the named nonparametric fallback when a check fails — normality, equal variance, and independence, with the fallback chosen up front.

  • /applied-statistics:choose-statistical-test

    Pick the right statistical test by traversing the decision tree on the outcome's data type, group count, and pairing, then check assumptions and drop to the named nonparametric fallback when the gate fails.

  • /applied-statistics:communicate-results-with-uncertainty

    Write up a statistical result for a non-statistician — lead with a plain-language bottom line tied to the decision, attach the effect size and CI, surface assumptions and caveats in the open, and date any volatile claim.

  • /applied-statistics:correct-multiple-comparisons

    Control the error rate across a family of tests — count every test in the family, then pick FWER (Holm/Bonferroni) for confirmatory decisions or FDR (Benjamini-Hochberg) for screening, before calling any one result significant.

  • /applied-statistics:design-experiment-and-power

    Design an experiment or A/B test and size it: pre-register the plan, fix three of {alpha, power, MDE, n} and solve for n, and set a stopping rule before any data is collected.

  • /applied-statistics:diagnose-regression

    Vet a regression before trusting its coefficients: pick the model family, run residual / multicollinearity / leverage diagnostics, and report adjusted R-squared or out-of-sample error honestly.

  • /applied-statistics:model-time-series

    Model or forecast a time series defensibly — test stationarity (ADF + KPSS), difference/detrend, identify orders from ACF/PACF, confirm residuals are white noise, validate temporally (never shuffle), and ship a prediction interval.

Subagents (1)

  • applied-statistician

    Use for applied-statistics judgment on real data — 'which test?', 'is this A/B winner real?', 'how big a sample?', 'signal or noise?', 'is it causal?'. Names the method before the library; reports effect size + CI. NOT for ML training (data-engineer).

Hooks (1)

  • PreToolUse

    /usr/bin/env -u BASH_ENV -u ENV -u SHELLOPTS -u BASHOPTS -u LD_PRELOAD -u LD_LIBRARY_PATH -u LD_AUDIT -u DYLD_INSERT_LIBRARIES -u DYLD_LIBRARY_PATH -u PYTHONPATH -u PYTHONSTARTUP PATH=/usr/local/bin:/

View source on GitHub mcorbett51090.github.io

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