{
  "schemaVersion": "1.0",
  "item": {
    "slug": "statistics",
    "name": "Statistics",
    "source": "tencent",
    "type": "skill",
    "category": "数据分析",
    "sourceUrl": "https://clawhub.ai/ivangdavila/statistics",
    "canonicalUrl": "https://clawhub.ai/ivangdavila/statistics",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadMode": "redirect",
    "downloadUrl": "/downloads/statistics",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=statistics",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "SKILL.md"
    ],
    "primaryDoc": "SKILL.md",
    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
        }
      ]
    },
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/statistics"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/statistics",
    "agentPageUrl": "https://openagent3.xyz/skills/statistics/agent",
    "manifestUrl": "https://openagent3.xyz/skills/statistics/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/statistics/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Detect Level, Adapt Everything",
        "body": "Context reveals level: notation familiarity, software mentioned, problem complexity\nWhen unclear, start with concrete examples and adjust based on response\nNever condescend to experts or overwhelm beginners"
      },
      {
        "title": "For Beginners: Intuition Before Formulas",
        "body": "Probability through physical objects — dice, coins, cards, colored balls in bags\nAverages as balance points — \"If everyone shared equally, each would get...\"\nVariation matters as much as center — two classes with same average, very different spreads\nGraphs before numbers — show the shape, then quantify it\nSampling as tasting soup — one spoonful tells you about the pot if well stirred\nCorrelation isn't causation — ice cream sales and drowning both rise in summer\nConnect to their decisions — weather forecasts, medical tests, sports statistics"
      },
      {
        "title": "For Students: Frameworks and Assumptions",
        "body": "Name the test AND its assumptions — normality, independence, equal variance\nEffect size alongside p-value — statistical significance ≠ practical importance\nConfidence intervals tell richer stories than hypothesis tests alone\nDistinguish population parameters from sample statistics — Greek vs Roman letters matter\nSimulation builds intuition — bootstrap, permutation tests show what formulas hide\nRegression diagnostics before interpretation — residual plots catch violations\nBayesian vs frequentist — acknowledge the philosophical divide, explain context for each"
      },
      {
        "title": "For Researchers: Rigor and Honesty",
        "body": "Pre-registration prevents p-hacking — specify analysis before seeing data\nPower analysis before collecting — underpowered studies waste resources\nMultiple comparisons require adjustment — Bonferroni, FDR, or justify why not\nReport effect sizes and confidence intervals — not just p-values\nMissing data mechanisms matter — MCAR, MAR, MNAR require different treatments\nCausal inference needs design — DAGs, potential outcomes, state assumptions explicitly\nReproducibility means code and data — \"available upon request\" is not reproducible"
      },
      {
        "title": "For Teachers: Common Misconceptions",
        "body": "p-value is NOT probability hypothesis is true — it's probability of data given null\nFailing to reject ≠ accepting null — absence of evidence isn't evidence of absence\nLarge samples don't fix bias — garbage in, garbage out regardless of n\nStandard deviation vs standard error — population spread vs sampling precision\nCorrelation coefficient hides nonlinearity — always plot first\nUse real messy data — textbook examples with clean answers mislead\nTeach skepticism — \"How was this measured? Who was sampled? What's missing?\""
      },
      {
        "title": "Always",
        "body": "Visualize data before computing anything\nState assumptions explicitly — every test has them\nDistinguish exploratory from confirmatory — same data can't do both"
      }
    ],
    "body": "Detect Level, Adapt Everything\nContext reveals level: notation familiarity, software mentioned, problem complexity\nWhen unclear, start with concrete examples and adjust based on response\nNever condescend to experts or overwhelm beginners\nFor Beginners: Intuition Before Formulas\nProbability through physical objects — dice, coins, cards, colored balls in bags\nAverages as balance points — \"If everyone shared equally, each would get...\"\nVariation matters as much as center — two classes with same average, very different spreads\nGraphs before numbers — show the shape, then quantify it\nSampling as tasting soup — one spoonful tells you about the pot if well stirred\nCorrelation isn't causation — ice cream sales and drowning both rise in summer\nConnect to their decisions — weather forecasts, medical tests, sports statistics\nFor Students: Frameworks and Assumptions\nName the test AND its assumptions — normality, independence, equal variance\nEffect size alongside p-value — statistical significance ≠ practical importance\nConfidence intervals tell richer stories than hypothesis tests alone\nDistinguish population parameters from sample statistics — Greek vs Roman letters matter\nSimulation builds intuition — bootstrap, permutation tests show what formulas hide\nRegression diagnostics before interpretation — residual plots catch violations\nBayesian vs frequentist — acknowledge the philosophical divide, explain context for each\nFor Researchers: Rigor and Honesty\nPre-registration prevents p-hacking — specify analysis before seeing data\nPower analysis before collecting — underpowered studies waste resources\nMultiple comparisons require adjustment — Bonferroni, FDR, or justify why not\nReport effect sizes and confidence intervals — not just p-values\nMissing data mechanisms matter — MCAR, MAR, MNAR require different treatments\nCausal inference needs design — DAGs, potential outcomes, state assumptions explicitly\nReproducibility means code and data — \"available upon request\" is not reproducible\nFor Teachers: Common Misconceptions\np-value is NOT probability hypothesis is true — it's probability of data given null\nFailing to reject ≠ accepting null — absence of evidence isn't evidence of absence\nLarge samples don't fix bias — garbage in, garbage out regardless of n\nStandard deviation vs standard error — population spread vs sampling precision\nCorrelation coefficient hides nonlinearity — always plot first\nUse real messy data — textbook examples with clean answers mislead\nTeach skepticism — \"How was this measured? Who was sampled? What's missing?\"\nAlways\nVisualize data before computing anything\nState assumptions explicitly — every test has them\nDistinguish exploratory from confirmatory — same data can't do both"
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/ivangdavila/statistics",
    "publisherUrl": "https://clawhub.ai/ivangdavila/statistics",
    "owner": "ivangdavila",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/statistics",
    "downloadUrl": "https://openagent3.xyz/downloads/statistics",
    "agentUrl": "https://openagent3.xyz/skills/statistics/agent",
    "manifestUrl": "https://openagent3.xyz/skills/statistics/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/statistics/agent.md"
  }
}