{
  "schema_version": "core-reviewed-term-ai-handoff-v2",
  "version": "core-reviewed-term-ai-handoff-v2",
  "compat": {
    "backward_compatible_schema_versions": [
      "core-reviewed-term-ai-handoff-v1"
    ],
    "v1_fields_preserved": true
  },
  "contract_status": "preview",
  "distribution": "per_term_read_only_ai_handoff",
  "no_write_operations": true,
  "content_tier": "db_backed",
  "canonical": {
    "url": "https://core.yogoq.com/ko-KR/core/hypothesis",
    "slug": "hypothesis",
    "locale": "ko-KR"
  },
  "summary": {
    "schema_version": "core-reviewed-term-summary-v1",
    "term_id": "32e29ee8-fb3c-5115-9f6e-37ad0c47f059",
    "canonical_slug": "hypothesis",
    "canonical_url": "https://core.yogoq.com/ko-KR/core/hypothesis",
    "locale": "ko-KR",
    "display_name": "仮説",
    "english_name": "Hypothesis",
    "abbreviation": null,
    "short_definition": "A hypothesis is a testable statement that can be evaluated with data, often framed as null and alternative hypotheses.",
    "content_tier": "db_backed",
    "quality": "reviewed",
    "publication_status": "published_reviewed",
    "version": "core-reviewed-term-summary-v1",
    "last_reviewed_at": "2026-05-14T00:00:00.000Z",
    "sources_count": 3,
    "limitations_key": "core-trust-policy-v1-2026-06-22"
  },
  "aliases": [
    {
      "text": "仮説",
      "type": "display_name",
      "locale": "ko-KR"
    },
    {
      "text": "Hypothesis",
      "type": "english_name",
      "locale": "en-US"
    },
    {
      "text": "ハイポセシス",
      "type": "katakana",
      "locale": "ko-KR"
    },
    {
      "text": "仮説",
      "type": "localized_title",
      "locale": "ja-JP"
    }
  ],
  "content": {
    "definition": {
      "key": "definition",
      "title": "一言でいうと",
      "text": "A hypothesis is a testable statement that can be evaluated with data, often framed as null and alternative hypotheses.",
      "items": []
    },
    "formula": null,
    "boundary": null,
    "usage": [
      {
        "key": "meaning",
        "title": "意味",
        "text": "A hypothesis defines a specific claim about a population or relationship that can be tested through evidence. In statistical testing, the null hypothesis represents no effect, while the alternative represents a meaningful difference or relationship. Clear hypotheses guide experiment design, sample size decisions, and interpretation of results.",
        "items": []
      },
      {
        "key": "usage",
        "title": "役立つ場面",
        "text": "It determines the experiment design and what data are required. It shapes which metrics and thresholds indicate success or failure. It influences how confidently results can be acted upon.",
        "items": [
          "It determines the experiment design and what data are required.",
          "It shapes which metrics and thresholds indicate success or failure.",
          "It influences how confidently results can be acted upon."
        ]
      },
      {
        "key": "usage",
        "title": "使い方のポイント",
        "text": null,
        "items": [
          "State hypotheses in measurable terms with defined variables.",
          "Specify null and alternative hypotheses before testing.",
          "Choose sample sizes that can detect meaningful effects.",
          "Interpret results in context, not just by p-values.",
          "Document assumptions so others can replicate the test."
        ]
      }
    ],
    "misunderstandings": [
      {
        "key": "misunderstandings",
        "title": "よくある誤解 / 落とし穴",
        "text": null,
        "items": [
          "A hypothesis is not a casual guess; it is a testable statement.",
          "Failing to reject the null does not prove the null is true.",
          "Changing hypotheses after seeing data undermines validity."
        ]
      }
    ],
    "examples": [
      {
        "key": "examples",
        "title": "最小例",
        "text": "A product team tests whether a new onboarding flow increases activation. The null hypothesis states there is no difference, and the alternative states activation increases by at least 5%. They run an A/B test with a sample size large enough to detect the effect. Results show a statistically significant increase, and the team rolls out the change while documenting assumptions and limitations.",
        "items": []
      }
    ],
    "comparisons": [
      {
        "key": "comparisons",
        "title": "似ている言葉との違い",
        "text": "Compare Hypothesis with adjacent concepts before deciding. Hypothesis | Current concept | Use when the team needs the primary decision lens Adjacent metric or framework | Supporting lens | Use when the team needs evidence or process detail General vocabulary | Broad explanation | Use only for orientation, not final decision-making",
        "items": [
          "Hypothesis | Current concept | Use when the team needs the primary decision lens",
          "Adjacent metric or framework | Supporting lens | Use when the team needs evidence or process detail",
          "General vocabulary | Broad explanation | Use only for orientation, not final decision-making"
        ]
      }
    ],
    "faq": [
      {
        "question": "When should I use Hypothesis?",
        "answer": "Use it when the team needs to decide scope, priority, owner, or trade-off, not when it only needs a short definition."
      },
      {
        "question": "What makes Hypothesis useful in practice?",
        "answer": "It becomes useful when it is tied to evidence, a decision owner, and a concrete next operating choice."
      },
      {
        "question": "What should I avoid?",
        "answer": "Avoid using the term as a label without clarifying assumptions, boundaries, and how success will be judged."
      }
    ]
  },
  "source_refs": [
    "concept:hypothesis:en-US",
    "concept:hypothesis:ja-JP",
    "core-term-page-500:concept:hypothesis"
  ],
  "sources": {
    "source_refs": [
      "concept:hypothesis:en-US",
      "concept:hypothesis:ja-JP",
      "core-term-page-500:concept:hypothesis"
    ],
    "visible_sources": [
      {
        "label": "Introductory Statistics 2e 9.1 Null and Alternative Hypotheses (OpenStax)",
        "url": "https://openstax.org/books/introductory-statistics-2e/pages/9-1-null-and-alternative-hypotheses",
        "kind": null
      },
      {
        "label": "Principles of Marketing (Open Textbook Library)",
        "url": "https://open.umn.edu/opentextbooks/textbooks/principles-of-marketing",
        "kind": "tier_s"
      },
      {
        "label": "Principles of Management (OpenStax)",
        "url": "https://openstax.org/details/books/principles-management",
        "kind": "tier_s"
      }
    ]
  },
  "related_terms": [
    {
      "topic_id": "d2dd0407-3546-5223-8dea-7e4da635fd39",
      "canonical_slug": "information-technology",
      "canonical_url": "https://core.yogoq.com/ko-KR/core/information-technology",
      "title": "情報技術(IT)",
      "relation_type": "next"
    },
    {
      "topic_id": "4505f577-d7f6-5036-8a4a-f358d0f1d377",
      "canonical_slug": "human-resources",
      "canonical_url": "https://core.yogoq.com/ko-KR/core/human-resources",
      "title": "人事",
      "relation_type": "used_together"
    },
    {
      "topic_id": "bf1c072f-38a8-5b40-8194-ee8f53ce67a7",
      "canonical_slug": "3c-analysis",
      "canonical_url": "https://core.yogoq.com/ko-KR/core/3c-analysis",
      "title": "3C分析",
      "relation_type": "used_together"
    },
    {
      "topic_id": "c2814f26-6ed6-50c2-bc84-a5e47d2c229a",
      "canonical_slug": "a-b-testing",
      "canonical_url": "https://core.yogoq.com/ko-KR/core/a-b-testing",
      "title": "A/Bテスト",
      "relation_type": "used_together"
    }
  ],
  "relations": [
    {
      "term_id": "d2dd0407-3546-5223-8dea-7e4da635fd39",
      "canonical_slug": "information-technology",
      "canonical_url": "https://core.yogoq.com/ko-KR/core/information-technology",
      "display_name": "情報技術(IT)",
      "relation_type": "related",
      "source_relation_type": "next"
    },
    {
      "term_id": "4505f577-d7f6-5036-8a4a-f358d0f1d377",
      "canonical_slug": "human-resources",
      "canonical_url": "https://core.yogoq.com/ko-KR/core/human-resources",
      "display_name": "人事",
      "relation_type": "related",
      "source_relation_type": "used_together"
    },
    {
      "term_id": "bf1c072f-38a8-5b40-8194-ee8f53ce67a7",
      "canonical_slug": "3c-analysis",
      "canonical_url": "https://core.yogoq.com/ko-KR/core/3c-analysis",
      "display_name": "3C分析",
      "relation_type": "related",
      "source_relation_type": "used_together"
    },
    {
      "term_id": "c2814f26-6ed6-50c2-bc84-a5e47d2c229a",
      "canonical_slug": "a-b-testing",
      "canonical_url": "https://core.yogoq.com/ko-KR/core/a-b-testing",
      "display_name": "A/Bテスト",
      "relation_type": "related",
      "source_relation_type": "used_together"
    }
  ],
  "chunk_ids": [
    "core:chunk:hypothesis:ko-KR:definition:9594bb3ab773d9ae",
    "core:chunk:hypothesis:ko-KR:meaning:a1120b8eba5ff061",
    "core:chunk:hypothesis:ko-KR:usage:8cfe8433a2d14adf",
    "core:chunk:hypothesis:ko-KR:usage:5cef80c3b678c9ea",
    "core:chunk:hypothesis:ko-KR:misunderstandings:8b2bd1f7eafa7fb4",
    "core:chunk:hypothesis:ko-KR:examples:8909582b50c3d1d0",
    "core:chunk:hypothesis:ko-KR:comparisons:87bdf3bd3204d64e",
    "core:chunk:hypothesis:ko-KR:faq:7e7faefb1610951c",
    "core:chunk:hypothesis:ko-KR:faq:9ee187ac31335d95",
    "core:chunk:hypothesis:ko-KR:faq:dbce09c620521df1"
  ],
  "chunks": [
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:definition:9594bb3ab773d9ae",
      "section_key": "definition",
      "heading": "一言でいうと",
      "text": "A hypothesis is a testable statement that can be evaluated with data, often framed as null and alternative hypotheses.",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "9594bb3ab773d9ae89eee86abd63aabdb4c13e354f4f42e841ad2f53c55e37b5"
    },
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:meaning:a1120b8eba5ff061",
      "section_key": "meaning",
      "heading": "意味",
      "text": "A hypothesis defines a specific claim about a population or relationship that can be tested through evidence. In statistical testing, the null hypothesis represents no effect, while the alternative represents a meaningful difference or relationship. Clear hypotheses guide experiment design, sample size decisions, and interpretation of results.",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "a1120b8eba5ff061a28423386ac41d3e7ee2b908eaf4dc34a594173884e0b866"
    },
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:usage:8cfe8433a2d14adf",
      "section_key": "usage",
      "heading": "役立つ場面",
      "text": "It determines the experiment design and what data are required. It shapes which metrics and thresholds indicate success or failure. It influences how confidently results can be acted upon. It determines the experiment design and what data are required. It shapes which metrics and thresholds indicate success or failure. It influences how confidently results can be acted upon.",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "8cfe8433a2d14adf8b8895b1dc7b6ae2c7585714f38504fe6d469dde66f3035a"
    },
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:usage:5cef80c3b678c9ea",
      "section_key": "usage",
      "heading": "使い方のポイント",
      "text": "State hypotheses in measurable terms with defined variables. Specify null and alternative hypotheses before testing. Choose sample sizes that can detect meaningful effects. Interpret results in context, not just by p-values. Document assumptions so others can replicate the test.",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "5cef80c3b678c9ead99b65fffef536b1fc955ee862bb888f2055f3d4c3f8330b"
    },
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:misunderstandings:8b2bd1f7eafa7fb4",
      "section_key": "misunderstandings",
      "heading": "よくある誤解 / 落とし穴",
      "text": "A hypothesis is not a casual guess; it is a testable statement. Failing to reject the null does not prove the null is true. Changing hypotheses after seeing data undermines validity.",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "8b2bd1f7eafa7fb4ed8838fd4c2a622b96b0bd8e55feb6a7a448be31377975af"
    },
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:examples:8909582b50c3d1d0",
      "section_key": "examples",
      "heading": "最小例",
      "text": "A product team tests whether a new onboarding flow increases activation. The null hypothesis states there is no difference, and the alternative states activation increases by at least 5%. They run an A/B test with a sample size large enough to detect the effect. Results show a statistically significant increase, and the team rolls out the change while documenting assumptions and limitations.",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "8909582b50c3d1d0bfcfaa8eca337628d52ad4ab7630b3fafa0b0b977e4ff51e"
    },
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:comparisons:87bdf3bd3204d64e",
      "section_key": "comparisons",
      "heading": "似ている言葉との違い",
      "text": "Compare Hypothesis with adjacent concepts before deciding. Hypothesis | Current concept | Use when the team needs the primary decision lens Adjacent metric or framework | Supporting lens | Use when the team needs evidence or process detail General vocabulary | Broad explanation | Use only for orientation, not final decision-making Hypothesis | Current concept | Use when the team needs the primary decision lens Adjacent metric or framework | Supporting lens | Use when the team needs evidence or process detail General vocabulary | Broad explanation | Use only for orientation, not final decision-making",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "87bdf3bd3204d64e2c9704a74ae222ac2d2377a2e518619418fc4d6eb5efdf91"
    },
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:faq:7e7faefb1610951c",
      "section_key": "faq",
      "heading": "When should I use Hypothesis?",
      "text": "When should I use Hypothesis? Use it when the team needs to decide scope, priority, owner, or trade-off, not when it only needs a short definition.",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "7e7faefb1610951c14e4ec7282974a83c6ad02066e1ed54c5aacc0319cdb529f"
    },
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:faq:9ee187ac31335d95",
      "section_key": "faq",
      "heading": "What makes Hypothesis useful in practice?",
      "text": "What makes Hypothesis useful in practice? It becomes useful when it is tied to evidence, a decision owner, and a concrete next operating choice.",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "9ee187ac31335d95d30bbe253b6156c9a2f6773d4ec785ad48b626ca06bb1d2f"
    },
    {
      "chunk_id": "core:chunk:hypothesis:ko-KR:faq:dbce09c620521df1",
      "section_key": "faq",
      "heading": "What should I avoid?",
      "text": "What should I avoid? Avoid using the term as a label without clarifying assumptions, boundaries, and how success will be judged.",
      "source_refs": [
        "concept:hypothesis:en-US",
        "concept:hypothesis:ja-JP",
        "core-term-page-500:concept:hypothesis"
      ],
      "content_hash": "dbce09c620521df15f5e3b3ad3ec8180cc0b31d1ee4c269ad04a24605cfd9f92"
    }
  ],
  "content_hash": "9d301d226763b9f926303453a3ff39661f2c8baab37f176c5f428a90881db414",
  "hash_algorithm": "sha256",
  "rag": {
    "chunk_id_policy": "core:chunk:{canonical_slug}:{locale}:{section_key}:{sha256_16}",
    "content_hash_policy": "sha256_stable_json",
    "source_refs_precision": "source_object_ref_or_source_key_current_snapshot"
  },
  "quality": {
    "content_tier": "db_backed",
    "quality": "reviewed",
    "publication_status": "published_reviewed",
    "source_count": 3,
    "last_reviewed_at": "2026-05-14T00:00:00.000Z",
    "trust_policy_version": "core-trust-policy-v1-2026-06-22"
  },
  "limitations": {
    "policy_version": "core-trust-policy-v1-2026-06-22",
    "professional_advice_boundary": "reference_only_not_professional_advice",
    "text": "이 페이지는 조사와 학습을 위한 참조 정보입니다. 회계, 법무, 금융, 의료, 보안 등 개별 판단은 1차 출처나 전문가 확인을 우선하세요.",
    "items": [
      "공개 페이지는 일반 이해와 실무 맥락을 돕는 자료이며, 개별 사안의 전문 조언이 아닙니다.",
      "제도, 가격, 규제, 회계 기준, 제품 사양처럼 빠르게 변하는 정보는 최종 판단 전에 1차 출처를 확인해야 합니다.",
      "AI 지원 제작·감사 흐름을 사용하더라도 공개 여부는 품질 게이트와 사람이 읽을 수 있는 증거에 기반해 다룹니다."
    ]
  }
}
