統計学者
コンピュータ・数学AI露出度
- 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尺度・母数・出典
- 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の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは8%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 28 件を表示| Task | Claude.aiRaw / share % |
|---|---|
Evaluate sources of information to determine any limitations in terms of reliability or usability.15-2041 | 0.399527.4 |
Read technical literature and participate in continuing education or professional associations to maintain awareness of current database technology and best practices.15-2041 | 0.13999.6 |
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 | 0.09986.8 |
Develop an understanding of fields to which statistical methods are to be applied to determine whether methods and results are appropriate.15-2041 | 0.09396.4 |
Draw conclusions or make predictions based on data summaries or statistical analyses.15-2041 | 0.08375.7 |
Prepare appropriate formatting to data sets as requested.15-2041 | 0.07595.2 |
Report results of statistical analyses, including information in the form of graphs, charts, and tables.15-2041 | 0.07585.2 |
Process large amounts of data for statistical modeling and graphic analysis, using computers.15-2041 | 0.05743.9 |
Identify relationships and trends in data, as well as any factors that could affect the results of research.15-2041 | 0.05343.7 |
Prepare data for processing by organizing information, checking for any inaccuracies, and adjusting and weighting the raw data.15-2041 | 0.05053.5 |
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 | 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 |
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
職業情報
この職業に関わる最近の変化
2026年3月: Published evergreen blog post analyzing AI impact on statistics: 78% exposure yet +30% BLS growth, causal inference and experimental design remain human.
[出典: AI Changing Work Blog]2026年3月: Blog post on survey statisticians references statisticians data. 83% exposure, 37% risk.
[出典: ACW Blog]