Will AI replace Statisticians?
On paper, AI could touch ~79% of the work in Statisticians — and unlike most jobs, it's already showing up in the real workday, not just the theory.
O*NET-SOC 15-2041
How your 44 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a high share of this job's tasks (~79%). By late 2025, real-world AI use had reached about 21% of its task activity (already common). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
Read this as a range, not a verdict
The signals here partly disagree — AI's theoretical reach (~79%) and its real-world use (~21%) tell different stories. AI-risk scores also shift a lot by which model does the rating (2.7%–51.5% in one 2026 study), so this is a direction of travel, not a fixed answer.
See all 44 tasks, ratedBased on real task-level AI scores — click to collapse
- Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
- Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.
- Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.
- Process large amounts of data for statistical modeling and graphic analysis, using computers.
- Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.
- Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.
- Develop and test experimental designs, sampling techniques, and analytical methods.
- Supervise and provide instructions for workers collecting and tabulating data.
- Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.
- Prepare and structure data warehouses for storing data.
- Develop software applications or programming for statistical modeling and graphic analysis.
- Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports.
- Calculate sample size requirements for clinical studies.
- Write program code to analyze data with statistical analysis software.
- Develop or implement data analysis algorithms.
- Prepare articles for publication or presentation at professional conferences.
- Write research proposals or grant applications for submission to external bodies.
- Design or maintain databases of biological data.
- Assign work to biostatistical assistants or programmers.
- Analyze and interpret statistical data to identify significant differences in relationships among sources of information.
- Identify relationships and trends in data, as well as any factors that could affect the results of research.
- Report results of statistical analyses, including information in the form of graphs, charts, and tables.
- Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.
- Report results of statistical analyses in peer-reviewed papers and technical manuals.
- Evaluate sources of information to determine any limitations, in terms of reliability or usability.
- Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used.
- Apply sampling techniques, or use complete enumeration bases to determine and define groups to be surveyed.
- Draw conclusions or make predictions, based on data summaries or statistical analyses.
- Analyze clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques.
- Read current literature, attend meetings or conferences, and talk with colleagues to keep abreast of methodological or conceptual developments in fields such as biostatistics, pharmacology, life sciences, and social sciences.
- Design research studies in collaboration with physicians, life scientists, or other professionals.
- Prepare tables and graphs to present clinical data or results.
- Provide biostatistical consultation to clients or colleagues.
- Review clinical or other medical research protocols and recommend appropriate statistical analyses.
- Determine project plans, timelines, or technical objectives for statistical aspects of biological research studies.
- Prepare statistical data for inclusion in reports to data monitoring committees, federal regulatory agencies, managers, or clients.
- Plan or direct research studies related to life sciences.
- Monitor clinical trials or experiments to ensure adherence to established procedures or to verify the quality of data collected.
- Collect data through surveys or experimentation.
- Apply research or simulation results to extend biological theory or recommend new research projects.
- Develop or use mathematical models to track changes in biological phenomena, such as the spread of infectious diseases.
- Analyze archival data, such as birth, death, and disease records.
- Design surveys to assess health issues.
- Teach graduate or continuing education courses or seminars in biostatistics.
- ⚠️ None — every core task is at least partly within AI's reach. The job won't vanish, but almost all of it changes.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.