{
 "@context": "https://schema.org",
 "@type": "DefinedTerm",
 "@id": "https://wulfkaal.github.io/entities/sampling",
 "identifier": "kaal:entity:sampling",
 "name": "Sampling",
 "termCode": "sampling",
 "inDefinedTermSet": {
  "@id": "https://wulfkaal.github.io/entities/index.json"
 },
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
 "dateModified": "2026-07-29",
 "canonicalForm": "https://wulfkaal.github.io/entities/sampling.md",
 "sha256": "63f92013a21b6d5860321594265c96daff66e849126ac16d08d1aaae841f3fcf",
 "additionalProperty": [
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   "name": "status",
   "value": "derived"
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   "name": "claim_count",
   "value": 2
  },
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   "value": 2
  },
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   "name": "year_span",
   "value": [
    "2014",
    "2024"
   ]
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 ],
 "subjectOf": [
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/2447306-007",
   "identifier": "kaal:claim:2447306-007",
   "text": "Enlarging the sample does not cure selection bias in non-statistical sampling: a bigger sample neither compensates for the bias of non-statistical techniques nor guarantees that the sample is representative.",
   "abstract": "Increasing the sample size does not necessarily compensate for the potential selection bias of non-statistical techniques or guarantee the representativeness of the sample.",
   "citation": "Wulf A. Kaal, Private Fund Disclosures Under the Dodd-Frank Act (2014). SSRN: https://ssrn.com/abstract=2447306",
   "datePublished": "2014",
   "claim_type": "failure",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "non-statistical or haphazard sampling techniques"
   ],
   "source_pdf_sha256": "0c950d73240845e78faf1c3ca0ab820fcc50556faf7ff07f7f773d0876f0be8a",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4855607-010",
   "identifier": "kaal:claim:4855607-010",
   "text": "GNN scalability on large real world graphs is a genuine trade off rather than an engineering gap: sampling methods lose influential neighbors while clustering methods lose structural patterns, so each remedy sacrifices part of the signal the model needs.",
   "abstract": "Scalability is a major concern for GNNs on large real-world graphs, as sampling methods may lose influential neighbors while clustering methods may lose structural patterns.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
   "claim_type": "failure",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "large real world graphs where full graph processing is infeasible"
   ],
   "source_pdf_sha256": "eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113",
   "status": "current"
  }
 ],
 "description": "2 claims in the published works of Wulf A. Kaal carry the concept tag 'sampling'. Derived node: a roster, not an adjudicated definition."
}