{
  "kind": "answer",
  "studySlug": "llmstxt-effect",
  "slug": "do-the-raw-citation-averages-show-any-real-advantage",
  "title": "Do the raw citation averages show any real advantage?",
  "description": "The published averages are 6.8 citations with llms.txt and 6.7 without it. These descriptive values alone do not establish practical importance, statistical significance, or a causal benefit.",
  "lastUpdated": "2026-09-04",
  "lastTested": "2026-03-14",
  "sourceStudyUrl": "/trakkr-research/llmstxt-effect",
  "sourceStudyTitle": "The llms.txt Effect",
  "claimIds": [
    "llmstxt-effect:avg-with",
    "llmstxt-effect:avg-without",
    "llmstxt-effect:p-value"
  ],
  "relatedSlugs": [
    "answer:is-llms-txt-a-leading-indicator-of-ai-readiness",
    "answer:what-is-the-best-way-to-read-the-null-result",
    "fact:government-and-academic-sites-barely-use-llms-txt",
    "tracker:llmstxt-adoption-by-sector-tracker"
  ],
  "methodologySummary": "Built from HTTP scans of 37,894 AI-cited domains, linked to 337,362 citations and 882 citation snapshots in the Trakkr corpus.",
  "limitations": [
    "This is an observational study. It measures correlation with citation outcomes, not a controlled experiment.",
    "Adoption is uneven by sector, so raw averages can hide category concentration in SaaS and developer tooling.",
    "The top-5,000 test returns p=0.85. The full scanned sample returns p<0.001 with reported r=-0.065. Neither establishes a causal effect. The public aggregates do not include confidence intervals or document the cohort for citation means and medians well enough to recompute the tests."
  ],
  "keywords": [
    "llms.txt",
    "llms txt effect",
    "AI citations",
    "does llms.txt work",
    "raw averages llms.txt",
    "llms.txt averages"
  ],
  "schemaHints": {
    "pageType": "Article",
    "includeDataset": true
  },
  "question": "Do the raw citation averages show any real advantage?",
  "directAnswer": "The published averages are 6.8 citations with llms.txt and 6.7 without it. These descriptive values alone do not establish practical importance, statistical significance, or a causal benefit.",
  "answerSummary": "The public release does not document the means cohort sufficiently to recompute either test.",
  "keyFacts": [
    {
      "label": "Average citations with llms.txt",
      "value": "6.8",
      "detail": "Mean citations for domains with llms.txt.",
      "claimId": "llmstxt-effect:avg-with"
    },
    {
      "label": "Average citations without llms.txt",
      "value": "6.7",
      "detail": "Mean citations for domains without llms.txt.",
      "claimId": "llmstxt-effect:avg-without"
    },
    {
      "label": "Top-5,000 Mann-Whitney p-value",
      "value": "0.85",
      "detail": "Subset of the top 5,000 cited domains; not the full-sample test.",
      "claimId": "llmstxt-effect:p-value"
    }
  ],
  "evidenceTable": [
    {
      "label": "Average citations with llms.txt",
      "value": "6.8",
      "note": "Mean citations for domains with llms.txt."
    },
    {
      "label": "Average citations without llms.txt",
      "value": "6.7",
      "note": "Mean citations for domains without llms.txt."
    },
    {
      "label": "Top-5,000 Mann-Whitney p-value",
      "value": "0.85",
      "note": "Subset of the top 5,000 cited domains; not the full-sample test."
    }
  ],
  "whyItMatters": "This answer matters because it turns a study finding into an operating rule teams can use when they decide what to publish, refresh, or measure next.",
  "whatToDo": [
    "Treat llms.txt as an optional housekeeping file, not a primary citation-growth lever.",
    "Prioritize answer quality, source coverage, and page structure before spending disproportionate effort on llms.txt.",
    "If you do publish llms.txt, measure discovery and crawl behavior directly instead of assuming it improved citation performance."
  ],
  "faqs": [
    {
      "question": "Do the raw citation averages show any real advantage?",
      "answer": "The published averages are 6.8 citations with llms.txt and 6.7 without it. These descriptive values alone do not establish practical importance, statistical significance, or a causal benefit."
    },
    {
      "question": "Which numbers from The llms.txt Effect matter most here?",
      "answer": "Average citations with llms.txt: 6.8. Mean citations for domains with llms.txt. Average citations without llms.txt: 6.7. Mean citations for domains without llms.txt."
    },
    {
      "question": "What should a team do next?",
      "answer": "Treat llms.txt as an optional housekeeping file, not a primary citation-growth lever. Prioritize answer quality, source coverage, and page structure before spending disproportionate effort on llms.txt. If you do publish llms.txt, measure discovery and crawl behavior directly instead of assuming it improved citation performance."
    }
  ]
}