Statisticians
Computer and Mathematical Occupations- O*NET-SOC コード
- 15-2041.00
Develop or apply mathematical or statistical theory and methods to collect, organize, interpret, and summarize numerical data to provide usable information. May specialize in fields such as biostatistics, agricultural statistics, business statistics, or economic statistics. Includes mathematical and survey statisticians.
- データ出典: BLS公表時点: 2026-08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.211
0.000ここに掲載された値の範囲0.745職業群単位の値
尺度・母数・出典
観測エクスポージャー指数、公開されたまま0–1
O*NETタスクへの対応づけが基準
SOC 2018の職業単位で公表された値で、同じSOC 2018コードを持つO*NET職業はすべてこの値になります
- データ出典: ILO公表時点: 2025
0.56
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の細分類（4桁）単位で公表された値です。米国労働統計局（BLS）の公式対応表（ISCO-08→2010 SOC、2010 SOC→2018 SOC）を公表どおり適用してこの職業に結び付けており、対応は全部または一部です
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
非表示の作業 7 件を表示評価済みタスク 19 件 · β ≥ 0.5 のタスク 19 件（100.0%）
β = 直接的な露出（E1）+ 0.5 × ツール利用時の露出（E2）。原典リポジトリの定義に従います。
- 原典の単位：O*NET 27.2 のタスク → O*NET 31.0 の職業コード
- 評価済みタスクはすべて O*NET 31.0 のタスク一覧にあります。
- 出典
- OpenAI "GPTs are GPTs" exposure rubric
- 版
- gh-main-0471612
- ライセンス
- MIT License, Copyright (c) 2024 OpenAI
| Task (O*NET 20.1) | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Evaluate sources of information to determine any limitations in terms of reliability or usability.15-2041.00 | 0.100427.9 | 0.288230.0 |
Prepare data for processing by organizing information, checking for any inaccuracies, and adjusting and weighting the raw data.15-2041.00 | 0.087424.3 | 0.546657.0 |
Report results of statistical analyses, including information in the form of graphs, charts, and tables.15-2041.00 | 0.02898.0 | 0.02072.2 |
Develop software applications or programming to use for statistical modeling and graphic analysis.15-2041.00 | 0.02737.6 | 0.01481.5 |
Develop and test experimental designs, sampling techniques, and analytical methods.15-2041.00 | 0.02717.5 | 0.00660.7 |
Process large amounts of data for statistical modeling and graphic analysis, using computers.15-2041.00 | 0.02617.3 | 0.02302.4 |
Identify relationships and trends in data, as well as any factors that could affect the results of research.15-2041.00 | 0.02256.3 | 0.03954.1 |
Design research projects that apply valid scientific techniques and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.15-2041.00 | 0.01564.3 | 0.01271.3 |
Develop an understanding of fields to which statistical methods are to be applied to determine whether methods and results are appropriate.15-2041.00 | 0.01373.8 | 0.00330.3 |
Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.15-2041.00 | 0.00601.7 | 0.00210.2 |
Report results of statistical analyses in peer-reviewed papers and technical manuals.15-2041.00 | 0.00491.4 | 0.00200.2 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task (O*NET 27.2) | βE1 + 0.5 × E2 |
|---|---|
Process large amounts of data for statistical modeling and graphic analysis, using computers.O*NET Task ID 8954 | 1.0 |
Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.O*NET Task ID 8957 | 1.0 |
Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.O*NET Task ID 8958 | 1.0 |
Supervise and provide instructions for workers collecting and tabulating data.O*NET Task ID 8963 | 1.0 |
Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.O*NET Task ID 8965 | 1.0 |
Develop and test experimental designs, sampling techniques, and analytical methods.O*NET Task ID 8966 | 1.0 |
Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.O*NET Task ID 8967 | 1.0 |
Develop software applications or programming for statistical modeling and graphic analysis.O*NET Task ID 20193 | 1.0 |
Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.O*NET Task ID 20194 | 1.0 |
Determine whether statistical methods are appropriate, based on user needs or research questions of interest.O*NET Task ID 21100 | 1.0 |
Prepare and structure data warehouses for storing data.O*NET Task ID 21101 | 1.0 |
Report results of statistical analyses, including information in the form of graphs, charts, and tables.O*NET Task ID 8953 | 0.5 |
Identify relationships and trends in data, as well as any factors that could affect the results of research.O*NET Task ID 8955 | 0.5 |
Analyze and interpret statistical data to identify significant differences in relationships among sources of information.O*NET Task ID 8956 | 0.5 |
Evaluate sources of information to determine any limitations, in terms of reliability or usability.O*NET Task ID 8959 | 0.5 |
Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used.O*NET Task ID 8960 | 0.5 |
Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.O*NET Task ID 8961 | 0.5 |
Apply sampling techniques, or use complete enumeration bases to determine and define groups to be surveyed.O*NET Task ID 8964 | 0.5 |
Report results of statistical analyses in peer-reviewed papers and technical manuals.O*NET Task ID 20192 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
O*NET 31.0 のタスク全件（19件）
この職業の O*NET 31.0 のタスク全件（英語原文、種別はコア・補足）。露出値はこの一覧には載せず、上の表で各出典が用いた O*NET 版の文言の横にあります。
| タスク（O*NET 31.0） | 種類 |
|---|---|
| Report results of statistical analyses, including information in the form of graphs, charts, and tables. | コア |
| Process large amounts of data for statistical modeling and graphic analysis, using computers. | コア |
| Identify relationships and trends in data, as well as any factors that could affect the results of research. | コア |
| Analyze and interpret statistical data to identify significant differences in relationships among sources of information. | コア |
| Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data. | コア |
| Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy. | コア |
| Evaluate sources of information to determine any limitations, in terms of reliability or usability. | コア |
| Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used. | コア |
| Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses. | コア |
| Supervise and provide instructions for workers collecting and tabulating data. | コア |
| Apply sampling techniques, or use complete enumeration bases to determine and define groups to be surveyed. | コア |
| Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering. | コア |
| Develop and test experimental designs, sampling techniques, and analytical methods. | コア |
| Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data. | コア |
| Report results of statistical analyses in peer-reviewed papers and technical manuals. | コア |
| Develop software applications or programming for statistical modeling and graphic analysis. | コア |
| Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students. | コア |
| Determine whether statistical methods are appropriate, based on user needs or research questions of interest. | コア |
| Prepare and structure data warehouses for storing data. | 補足 |
0.00/ 3· ロボット露出指数
尺度・母数・出典
身体的なタスクそれぞれに、実際に導入・販売・実演されたロボットがこなせる環境に応じて 0–3 の等級を付け（0 不可 · 1 フェンスで囲われた機械セルや瓶詰めラインのようなロボット専用設備 · 2 病院の薬剤部やコンテナ港のような管理された施設 · 3 一般家庭や建設現場のような非定型の環境）、勤務時間に占める割合で平均した値
この職業の O*NET 29.3 タスクのうち身体的なタスクが対象
等級と時間の割合は Anthropic が Claude Opus 5（ウェブ検索）で推定しました（2026年時点）。
ロボット露出指数は、このサイトの AI 露出度とは別の指標であり、足し合わせたり比べたりすることはできません。実際の導入やコスト競争力を示すものではなく、0 には身体的なタスクのない職業も含まれます。
身体的なタスクごとの等級
この職業の O*NET 29.3 タスクのうち、身体的なタスクに分類されたものはありません。
職業情報
雇用見通し（BLS）
この O*NET 職業だけの値ではなく、SOC 15-2041 グループ（Statisticians）全体の値です。
- 雇用者数、2025
- 31K
- 雇用者数見通し、2035
- 35K
- 変化率 (2025–35)
- +11%
- 年間賃金の中央値、2025
- $105,650
- 入職時に通常必要な学歴
- Master's degree
韓国職業分類との対応（KSCO）
AI Changing Work が作成した Korea Occupation Direct Crosswalk v1.1 現行版の対応です。関係の値は O*NET-SOC 職業の側から読みます。
| KSCO コード | KSCO 名称 | 関係 | 状態 |
|---|---|---|---|
| 21123 | 수학 및 통계학 연구원 | この職業のほうが狭い | 確定 |
関係と状態の定義(kr-direct v1.1)
ILO（国際労働機関）の職業説明
以下のISCO-08職業群は、AI Changing Workが米国労働統計局（BLS）の発行するクロスウォーク（2018 SOC→2010 SOC、2010 SOC→ISCO-08）を個別の選択なしにそのまま適用して、この職業に結び付けたものです。結び付きは全体的な対応の場合も部分的な対応の場合もあります。
- ISCO-08 2120Mathematicians, Actuaries and Statisticians
数学者、保険計理士、統計の専門職
出典: 総務省「国際標準職業分類（ISCO）2008年改定版（仮訳）分類項目名一覧（和英対照）」— 原著：ILO ISCO-08 © International Labour Organization（日本語訳は総務省がILOの許可を得て作成）
以下の定義・作業・職業例は、この職業ではなく、当該ISCO-08職業群についてのILOによる説明です。
Mathematicians, actuaries and statisticians conduct research and improve or develop mathematical, actuarial and statistical concepts, theories and operational methods and techniques and advise on or engage in their practical application in such fields as engineering, business and social and other sciences.
作業
- (a) studying, improving and developing mathematical, actuarial and statistical theories and techniques;
- (b) advising on or applying mathematical principles, models and techniques to a wide range of tasks in the fields of engineering, natural, social or life sciences;
- (c) conducting logical analyses of management problems, especially in terms of input-output effectiveness, and formulating mathematical models of each problem usually for programmeming and solution by computer;
- (c) designing and putting into operation pension schemes and life, health, social and other types of insurance systems;
- (e) applying mathematics, statistics, probability and risk theory to assess potential financial impacts of future events;
- (f) planning and organizing surveys and other statistical collections, and designing questionnaires;
- (g) evaluating, processing, analysing, and interpreting statistical data and preparing them for publication;
- (h) advising on or applying various data collection methods and statistical methods and techniques, and determining reliability of findings, especially in such fields as business or medicine as well as in other areas of natural, social or life sciences;
- (i) preparing scientific papers and reports;
- (j) supervising the work of mathematical, actuarial and statistical assistants and statistical clerks.
職業例
Actuary · Demographer · Mathematician · Operations research analyst · Statistician
Sources: ILO ISCO-08 — details: Credits
ESCO 職業語彙
以下の各項目は、公開されているESCO-O*NET Crosswalk（バージョン1、2022年）で、この職業と一つずつ対応付けられたものです。対応の種類はそのクロスウォークによるもので、表現はAI Changing Workによるものです。
- statisticianESCO 2120.6範囲が同じ
Statisticians collect, tabulate, and, most importantly, analyse quantitative information coming from a varied array of fields. They interpret and analyse statistical studies on fields such as health, demographics, finance, business, etc. and advise based on patterns and drawn analysis.
- biometricianESCO 2120.6.1この職業のほうが広い
Biometricians perform research in the field of biometrics. They carry statistical or biological research projects by measuring fingerprints, retinas, and human shapes for medical or industrial applications.
- data scientistESCO 2511.3重なるが同じではない
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
- statistical assistantESCO 3314.2重なるが同じではない
Statistical assistants collect data and use statistical formulas to execute statistical studies and create reports. They create charts, graphs and surveys.
出典: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)
ライセンス: CC BY 4.0 (EU Reuse Notice)
ESCO reuse terms and the modification notice are in Credits.
フランス職業分類との対応（ROME）
この職業と ROME 4.0 の職業票（fiche）との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。「暫定」と表示した対応は、同じモデル系列（Claude）の二人の判定者による判定のみ、またはその判定に運営者の規則を適用して採用したものであり、確定した対応より根拠が弱くなります。
| ROME コード | ROME 名称（フランス語） | 関係 |
|---|---|---|
| M1414 | Ingénieur statisticien / Ingénieure statisticienne | 範囲が同じ暫定 |
| K2403 | Biostatisticien / Biostatisticienne | この職業のほうが広い暫定 |
| M1403 | Chargé / Chargée d'études socio-économiques | 重なるが同じではない暫定 |
Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.
ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.
(AI Changing Work link release: fr-direct v1)
スペイン職業分類との対応（CNO-11）
この職業と CNO-11 の職業との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。
| CNO-11 コード | CNO-11 名称（スペイン語） | 関係 |
|---|---|---|
| 2416 | Estadísticos | 範囲が同じ |
Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.
(AI Changing Work link release: es-direct v1)
ドイツ職業分類との対応（KldB）
この職業と KldB の職業との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。
| KldB コード | KldB 名称（ドイツ語） | 関係 |
|---|---|---|
| 41114 | Berufe in der Statistik - hoch komplexe Tätigkeiten | 範囲が同じ |
Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.
(AI Changing Work link release: de-direct v1)
日本職業分類との対応（JSCO）
この職業と JSCO の職業との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。
| JSCO コード | JSCO 名称（日本語） | 関係 |
|---|---|---|
| 051 | 自然科学系研究者 | 関連（対応ではない） |
| 052 | 人文・社会科学系等研究者 | 関連（対応ではない） |
Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.
(AI Changing Work link release: jp-direct v1)
このページのデータの出典・ライセンスと AI Changing Work による改変・翻訳の告知：クレジットと出典