{
  "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 it as a prioritization signal, not as proof that the file is useless. The best interpretation is that llms.txt is low-cost optional hygiene, not a core citation lever.",
  "lastUpdated": "2026-03-14",
  "lastTested": "2026-03-14",
  "sourceStudyUrl": "/trakkr-research/llmstxt-effect",
  "sourceStudyTitle": "The llms.txt Effect",
  "claimIds": [
    "llmstxt-effect:p-value",
    "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.",
    "A null citation effect does not mean llms.txt has zero operational value for every workflow. It means the study did not find a measurable citation lift."
  ],
  "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": "Mostly, read it as a prioritization signal rather than proof that the file is useless. The best interpretation is that llms.txt is low-cost optional hygiene, not a core citation lever.",
  "answerSummary": "The study helps teams avoid overinvesting in a tactic that currently shows no measurable lift, allowing resources to be directed toward proven citation drivers.",
  "keyFacts": [
    {
      "label": "Mann-Whitney p-value",
      "value": "0.85",
      "detail": "No statistically significant citation effect detected.",
      "claimId": "llmstxt-effect:p-value"
    },
    {
      "label": "Adoption rate",
      "value": "13.3%",
      "detail": "Domains with llms.txt in the study.",
      "claimId": "llmstxt-effect:adoption"
    },
    {
      "label": "Domains scanned",
      "value": "37,894",
      "detail": "AI-cited domains scanned for llms.txt.",
      "claimId": "llmstxt-effect:domains"
    }
  ],
  "evidenceTable": [
    {
      "label": "Mann-Whitney p-value",
      "value": "0.85",
      "note": "No statistically significant citation effect detected."
    },
    {
      "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 turns a study finding into an operating rule teams can use when deciding what to publish, refresh, or measure next, preventing wasted engineering cycles on unproven optimization tactics.",
  "whatToDo": [
    "Treat llms.txt as an optional housekeeping file rather than a primary citation-growth lever.",
    "Prioritize answer quality, source coverage, and page structure before spending disproportionate effort on llms.txt.",
    "Measure discovery and crawl behavior directly if you publish llms.txt instead of assuming it improved citation performance."
  ],
  "faqs": [
    {
      "question": "Does the null result mean we should delete our existing llms.txt file?",
      "answer": "No, the Mann-Whitney p-value of 0.85 indicates no statistically significant citation effect was detected, but the file remains low-cost optional hygiene."
    },
    {
      "question": "How many domains are actually using this file?",
      "answer": "The study found an adoption rate of 13.3 percent across the 37,894 AI-cited domains scanned."
    }
  ]
}