Will AI replace statisticians?
Updated
Statisticians score 63 of 100 for AI exposure. The largest share of their task time, 74%, is work AI speeds up while they stay in charge. AI can take over 22% outright, and 4% stays with people.
- 22% AI can do it
- 74% AI helps
- 4% Needs a person
High exposure
Higher than 94% of the 923 occupations we scored.
Task by task
We graded 19 tasks, listed here from most to least exposed. AI can do 4 of them today, speeds up 14 and leaves 1 to people.
| Task | AI exposure |
|---|---|
| Report results of statistical analyses, including information in the form of graphs, charts, and tables. Code tools generate charts, tables, and summary text quickly. | 72% AI can do it |
| Process large amounts of data for statistical modeling and graphic analysis, using computers. Code tools process large datasets and generate graphics reliably. | 72% AI can do it |
| Develop software applications or programming for statistical modeling and graphic analysis. AI writes most modeling code; statistician reviews and tests output. | 70% AI can do it |
| Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data. Code tools handle cleaning, error checks, and routine weighting well. | 68% AI can do it |
| Report results of statistical analyses in peer-reviewed papers and technical manuals. AI drafts manuscript sections; authors verify claims and remain accountable. | 58% AI helps |
| Prepare and structure data warehouses for storing data. AI writes schemas and pipelines; engineer designs architecture and validates. | 58% AI helps |
| Analyze and interpret statistical data to identify significant differences in relationships among sources of information. AI runs comparisons fast; statistician judges significance and context. | 56% AI helps |
| Identify relationships and trends in data, as well as any factors that could affect the results of research. AI spots patterns quickly; statistician separates real trends from artifacts. | 54% AI helps |
| Evaluate sources of information to determine any limitations, in terms of reliability or usability. AI screens sources quickly; analyst judges reliability and usability limits. | 50% AI helps |
| Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy. AI can flag design weaknesses; validity judgment needs expert accountability. | 47% AI helps |
Show the other 9 tasks
| Task | AI exposure |
|---|---|
| Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering. AI suggests adaptations; domain expertise decides which methods truly fit. | 46% AI helps |
| Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students. AI drafts slides and talking points; presenter handles live audience. | 45% AI helps |
| Apply sampling techniques, or use complete enumeration bases to determine and define groups to be surveyed. Software draws samples; statisticians validate frames and coverage gaps. | 44% AI helps |
| Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used. AI computes sample sizes and drafts plans; planner weighs field constraints. | 41% AI helps |
| Determine whether statistical methods are appropriate, based on user needs or research questions of interest. AI suggests candidate methods; statistician must confirm fit to questions. | 40% AI helps |
| Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses. AI drafts analysis plans; researcher is responsible for design choices and tradeoffs. | 39% AI helps |
| Develop and test experimental designs, sampling techniques, and analytical methods. AI drafts designs and simulations; statistician selects and tests methods. | 38% AI helps |
| Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data. AI helps check derivations; novel theory needs expert researchers. | 33% AI helps |
| Supervise and provide instructions for workers collecting and tabulating data. Supervision of field staff needs live human judgment and accountability. | 20% Needs a person |
How statisticians compare
All occupations (923)
Computer and mathematical (36)
Pay and outlook in the US
- Median pay
- $105,650 a year, May 2025
- People employed
- 29,030 in the US, May 2025
- Projected change in jobs
- +11.0% from 2024 to 2034
Skills to build
- Hold the ASA Accredited Professional Statistician credential
- Validate AI-written modeling code in IBM SPSS Statistics
- Design sampling plans and sample sizes for field studies
- Supervise field staff who collect and tabulate data
- Build and validate data pipelines in Apache Spark
- Query and audit warehouse tables in Amazon Redshift
Safer roles nearby
5 related occupations with lower scores.
Questions about statisticians and AI
Will statisticians be replaced by AI?
Partly. About 5 in 10 working hours go to tasks AI can already do, mostly reporting analysis results and processing large datasets. What stays with people is choosing the right method for the question and standing behind the conclusions.
Which tasks of statisticians is AI taking over?
Reporting results of statistical analyses is the most exposed task, with an exposure score of 72. Code tools generate charts, tables, and summary text in minutes. Processing large datasets for modeling and graphics also scores 72, since code tools handle it reliably. Data preparation scores 68, because cleaning, error checks, and routine weighting now run through the same tools.
Which tasks of statisticians stay human?
Supervising workers who collect and tabulate data stays with people, because it needs live judgment and someone accountable for field results. Choosing statistical methods also stays human, AI suggests candidates, but the statistician must confirm each one fits the research question.
How can statisticians stay valuable?
Keep the decisions that carry the most weight, sampling designs, method selection, and field constraints. Test every AI-written modeling script in IBM SPSS Statistics before results leave the team. Build skill in Apache Spark and Amazon Redshift so you can validate the data pipelines AI writes.
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