{
 "@context": "https://schema.org",
 "@type": "DefinedTerm",
 "@id": "https://wulfkaal.github.io/entities/p-values",
 "identifier": "kaal:entity:p-values",
 "name": "P values",
 "termCode": "p-values",
 "inDefinedTermSet": {
  "@id": "https://wulfkaal.github.io/entities/index.json"
 },
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "canonicalForm": "https://wulfkaal.github.io/entities/p-values.md",
 "sha256": "8df8ac0d01586e89bcc4dea8d6c47c5044cdf70de2bd569ba6bef7230406adef",
 "additionalProperty": [
  {
   "@type": "PropertyValue",
   "name": "status",
   "value": "derived"
  },
  {
   "@type": "PropertyValue",
   "name": "claim_count",
   "value": 3
  },
  {
   "@type": "PropertyValue",
   "name": "work_count",
   "value": 2
  },
  {
   "@type": "PropertyValue",
   "name": "year_span",
   "value": [
    "2016",
    "2026"
   ]
  },
  {
   "@type": "PropertyValue",
   "name": "non_current_claims",
   "value": 0
  }
 ],
 "subjectOf": [
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/2816408-019",
   "identifier": "kaal:claim:2816408-019",
   "text": "Across an array of robustness tests, the requirements introduced by the Dodd-Frank Act create no significant effect on private fund performance, with all reported RD p-values above the 5% level.",
   "abstract": "Using an array of robustness tests validating our RD results, Figures 3-7 suggest that the requirements introduced by the Dodd-Frank Act create no significant effect on private fund performance. The P-values for all RD results in Table 6 are above the 5% level and confirm our finding of no effect.",
   "citation": "Wulf A. Kaal, Barbara Luppi, Sandra Paterlini, Did the Dodd-Frank Act Impact Private Fund Performance  – Evidence from 2010 – 2015 (2016). SSRN: https://ssrn.com/abstract=2816408",
   "datePublished": "2016",
   "claim_type": "empirical",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "RD results reported in Table 6",
    "entire sample and March 2012 estimates"
   ],
   "source_pdf_sha256": "dc3b2f9e60e42ea1b42c2e0062d65569675b014c05fcd30b15ad44d2410b2ce7",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/7598518-034",
   "identifier": "kaal:claim:7598518-034",
   "text": "A substrate was declared procedure-invalid by the operator before any run or reading, with no pre-run rule fired and no written reason recorded, and its nine registered tests stand at a p-value of one with no other label, so the filter is tested on one substrate, a two-dimensional mixture with simulated validators.",
   "abstract": "Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.",
   "citation": "Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518",
   "datePublished": "2026",
   "claim_type": "empirical",
   "confidence": "stated",
   "is_failure_mode": true,
   "scope_conditions": [
    "The paper draws no inference from the invalidated substrate.",
    "The declaration is one of two departures from the frozen procedure that the paper discloses."
   ],
   "source_pdf_sha256": "cd22d148b2b55c2e27f087dcaa61cdf44ad7022b400b355c31a9cd7e43a31749",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/7598518-037",
   "identifier": "kaal:claim:7598518-037",
   "text": "A contrast is superior when its Holm-adjusted p-value is below 0.05, its 95 percent interval lies above zero, and its point estimate is at least 0.10; it is negative when the adjusted p-value is below 0.05 and the interval lies below zero, a substantive null when its 90 percent interval lies within plus or minus that margin, and inconclusive otherwise.",
   "abstract": "Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.",
   "citation": "Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518",
   "datePublished": "2026",
   "claim_type": "design",
   "confidence": "stated",
   "is_failure_mode": false,
   "scope_conditions": [
    "The unit of analysis is the seed, and every contrast is a paired seed-level difference within a validator family.",
    "The effect sizes were fixed by the operator before any pilot."
   ],
   "source_pdf_sha256": "cd22d148b2b55c2e27f087dcaa61cdf44ad7022b400b355c31a9cd7e43a31749",
   "status": "current"
  }
 ],
 "description": "3 claims in the published works of Wulf A. Kaal carry the concept tag 'p-values'. Derived node: a roster, not an adjudicated definition."
}