{
  "kind": "answer",
  "studySlug": "llmstxt-effect",
  "slug": "what-is-the-best-way-to-read-the-null-result",
  "title": "What is the best way to read the null result?",
  "description": "Read p=0.85 as a non-significant test in the top 5,000 cited domains. The full sample returns p<0.001 with reported r=-0.065. Neither establishes that the causal effect is exactly zero.",
  "lastUpdated": "2026-09-04",
  "lastTested": "2026-03-14",
  "sourceStudyUrl": "/trakkr-research/llmstxt-effect",
  "sourceStudyTitle": "The llms.txt Effect",
  "claimIds": [
    "llmstxt-effect:p-value",
    "llmstxt-effect:full-p-value",
    "llmstxt-effect:effect-size",
    "llmstxt-effect:adoption",
    "llmstxt-effect:domains"
  ],
  "relatedSlugs": [
    "answer:what-should-teams-do-after-seeing-the-llms-txt-data",
    "answer:does-llms-txt-look-like-a-sector-specific-behavior-more-than-a-universal-standard",
    "fact:median-citations-are-identical-with-and-without-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",
    "null result llms.txt",
    "how to interpret llms.txt study"
  ],
  "schemaHints": {
    "pageType": "Article",
    "includeDataset": true
  },
  "question": "What is the best way to read the null result?",
  "directAnswer": "Read p=0.85 as a non-significant test in the top 5,000 cited domains. The full sample returns p<0.001 with reported r=-0.065. Neither establishes that the causal effect is exactly zero.",
  "answerSummary": "Keep the two cohorts separate and distinguish statistical significance from causal evidence.",
  "keyFacts": [
    {
      "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"
    },
    {
      "label": "Full-sample Mann-Whitney p-value",
      "value": "<0.001",
      "detail": "Full 37,894-domain sample. The public JSON rounds this value to zero.",
      "claimId": "llmstxt-effect:full-p-value"
    },
    {
      "label": "Full-sample reported effect size r",
      "value": "-0.065",
      "detail": "Small reported magnitude; not an estimate of causal citation lift.",
      "claimId": "llmstxt-effect:effect-size"
    },
    {
      "label": "Adoption rate",
      "value": "13.3%",
      "detail": "Domains with llms.txt in the study.",
      "claimId": "llmstxt-effect:adoption"
    }
  ],
  "evidenceTable": [
    {
      "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."
    },
    {
      "label": "Full-sample Mann-Whitney p-value",
      "value": "<0.001",
      "note": "Full 37,894-domain sample. The public JSON rounds this value to zero."
    },
    {
      "label": "Full-sample reported effect size r",
      "value": "-0.065",
      "note": "Small reported magnitude; not an estimate of causal citation lift."
    },
    {
      "label": "Adoption rate",
      "value": "13.3%",
      "note": "Domains with llms.txt in the study."
    },
    {
      "label": "Domains scanned",
      "value": "37,894",
      "note": "AI-cited domains scanned for llms.txt."
    }
  ],
  "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": "What is the best way to read the null result?",
      "answer": "Read p=0.85 as a non-significant test in the top 5,000 cited domains. The full sample returns p<0.001 with reported r=-0.065. Neither establishes that the causal effect is exactly zero."
    },
    {
      "question": "Which numbers from The llms.txt Effect matter most here?",
      "answer": "Top-5,000 Mann-Whitney p-value: 0.85. Subset of the top 5,000 cited domains; not the full-sample test. Full-sample Mann-Whitney p-value: <0.001. Full 37,894-domain sample. The public JSON rounds this value to zero."
    },
    {
      "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."
    }
  ]
}