{
  "kind": "answer",
  "studySlug": "llmstxt-effect",
  "slug": "why-is-llms-txt-getting-so-much-attention-if-the-effect-is-null",
  "title": "Why is llms.txt getting so much attention if the effect is null?",
  "description": "The premise needs care: only the top-5,000 test is non-significant. The full-sample test is significant with a small reported effect size. The study does not measure why people adopt the file.",
  "lastUpdated": "2026-09-04",
  "lastTested": "2026-03-14",
  "sourceStudyUrl": "/trakkr-research/llmstxt-effect",
  "sourceStudyTitle": "The llms.txt Effect",
  "claimIds": [
    "llmstxt-effect:adoption",
    "llmstxt-effect:p-value"
  ],
  "relatedSlugs": [
    "answer:do-the-raw-citation-averages-show-any-real-advantage",
    "answer:is-llms-txt-a-leading-indicator-of-ai-readiness",
    "fact:saas-and-developer-sites-adopt-llms-txt-far-more-often",
    "tracker:llmstxt-adoption-by-tier-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",
    "llms.txt hype",
    "why people use llms.txt"
  ],
  "schemaHints": {
    "pageType": "Article",
    "includeDataset": true
  },
  "question": "Why is llms.txt getting so much attention if the effect is null?",
  "directAnswer": "The premise needs care: only the top-5,000 test is non-significant. The full-sample test is significant with a small reported effect size. The study does not measure why people adopt the file.",
  "answerSummary": "A subset result cannot establish an exact null effect or explain public interest.",
  "keyFacts": [
    {
      "label": "Adoption rate",
      "value": "13.3%",
      "detail": "Domains with llms.txt in the study.",
      "claimId": "llmstxt-effect:adoption"
    },
    {
      "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": "Adoption rate",
      "value": "13.3%",
      "note": "Domains with llms.txt in the study."
    },
    {
      "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": "Why is llms.txt getting so much attention if the effect is null?",
      "answer": "The premise needs care: only the top-5,000 test is non-significant. The full-sample test is significant with a small reported effect size. The study does not measure why people adopt the file."
    },
    {
      "question": "Which numbers from The llms.txt Effect matter most here?",
      "answer": "Adoption rate: 13.3%. Domains with llms.txt in the study. Top-5,000 Mann-Whitney p-value: 0.85. Subset of the top 5,000 cited domains; not the full-sample test."
    },
    {
      "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."
    }
  ]
}