applied-statistics

v0.6.3

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, statistical QA of dashboard metrics, and multiple-comparison correction (FWER vs FDR). skills, a knowledge bank (Mermaid trees for test-selection, parametric-vs-nonparametric, regression, causal, time-series, and multiplicity-correction; a pitfalls guardrail; experiment-design; tooling tiers; a causal primer), a scenarios bank, a stdlib calculator (stat_calc.py: sample-size, p-value correction, effect-size, CI), templates, and an advisory hook. Python-first (scipy / statsmodels / pingouin Tier 1; PyMC Tier 2). Seams with data-platform. Method-before-library; report effect size + CI. Requires ravenclaude-core@>=0.7.0.

Claude Code5 Skills

By Matt CorbettLicense: MIT7 GitHub starsUpdated last week

Directory evidence

Runtimes: Claude Code
Parsed components: 5 skill or MCP entries
Source updated: Sep 15, 2026
Manifest status: Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →

Install applied-statistics for Claude Code

Installs for the current user

claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install applied-statistics@agent-plugin-marketplace

Paste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually

$ git clone https://github.com/mcorbett51090/RavenClaude

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/applied-statistics/.

Plugin files

plugins/applied-statistics/

├── .claude-plugin/plugin.json
├── skills/choose-statistical-test/SKILL.md
├── skills/experiment-analysis/SKILL.md
├── skills/power-and-sample-size/SKILL.md
├── skills/regression-and-forecasting-review/SKILL.md
└── skills/statistical-qa-of-metrics/SKILL.md

Included Skills5

choose-statistical-testskills/choose-statistical-test/SKILL.md

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 the user asks "which test do I use?" or hands over two-or-more groups/variables to compare. Used by `applied-statistician` (primary).

experiment-analysisskills/experiment-analysis/SKILL.md

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 before declaring a winner. Used by `applied-statistician` (primary).

power-and-sample-sizeskills/power-and-sample-size/SKILL.md

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 assumptions behind it, and a runnable snippet. Used by `applied-statistician` (primary).

regression-and-forecasting-reviewskills/regression-and-forecasting-review/SKILL.md

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, and "coefficient = cause" overreach. Used by `applied-statistician` (primary).

statistical-qa-of-metricsskills/statistical-qa-of-metrics/SKILL.md

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 needs a statistical-validity annotation. data-platform answers "is this number correct?"; this skill answers "is it real?". Used by `applied-statistician` (primary) + `data-platform/dashboard-builder`.

Plugin manifests1

plugins/applied-statistics/.claude-plugin/plugin.json

{
  "name": "applied-statistics",
  "version": "0.6.3",
  "description": "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, statistical QA of dashboard metrics, and multiple-comparison correction (FWER vs FDR). skills, a knowledge bank (Mermaid trees for test-selection, parametric-vs-nonparametric, regression, causal, time-series, and multiplicity-correction; a pitfalls guardrail; experiment-design; tooling tiers; a causal primer), a scenarios bank, a stdlib calculator (stat_calc.py: sample-size, p-value correction, effect-size, CI), templates, and an advisory hook. Python-first (scipy / statsmodels / pingouin Tier 1; PyMC Tier 2). Seams with data-platform. Method-before-library; report effect size + CI. Requires ravenclaude-core@>=0.7.0.",
  "author": {
    "name": "Matt Corbett"
  },
  "homepage": "https://github.com/mcorbett51090/RavenClaude",
  "license": "MIT",
  "keywords": [
    "applied-statistics",
    "hypothesis-testing",
    "ab-testing",
    "experiment-design",
    "power-analysis",
    "regression",
    "forecasting",
    "causal-inference",
    "statistical-qa",
    "p-hacking",
    "scipy",
    "statsmodels",
    "pingouin"
  ],
  "requires": {
    "plugins": [
      "ravenclaude-core@>=0.7.0"
    ]
  }
}

For maintainers

If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.

[applied-statistics on Agent Plugins Marketplace](https://pluginsmp.com/plugins/applied-statistics)