Statisticians

Identify relationships and trends in data, as well as any factors that could affect the results of research.

AI: Fully automatable - AI can identify relationships, trends, and potential confounders in datasets using modern statistical and machine‑learning methods with high accuracy when data quality is sufficient.

Report results of statistical analyses, including information in the form of graphs, charts, and tables.

AI: Fully automatable - AI systems can automatically generate written reports and produce graphs, charts, and tables from analysis outputs, enabling full automation of reporting tasks.

Analyze and interpret statistical data to identify significant differences in relationships among sources of information.

AI: Fully automatable - AI can run statistical tests, compare relationships across sources, and produce interpretable summaries that identify significant differences, allowing full automation in many cases.

Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.

AI: Fully automatable - Automated data-wrangling tools and AI can perform organizing, error checking, adjustment, and weighting of raw data at scale, making this task largely automatable.

Process large amounts of data for statistical modeling and graphic analysis, using computers.

AI: Fully automatable - Processing large datasets for statistical modeling and visualization is routine for computers and AI, enabling full automation of this task.

Apply sampling techniques or use complete enumeration bases to determine and define groups to be surveyed.

AI: Fully automatable - Sampling design and defining survey groups are algorithmic tasks that AI can fully perform given data, constraints and sampling objectives, including stratification and weighting schemes.

Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.

AI: Partial - Adapting statistical methods to novel, domain-specific problems often requires contextual judgment and creativity, so AI can assist substantially but not fully replace expert statisticians.

Develop software applications or programming to use for statistical modeling and graphic analysis.

AI: Partial - AI can generate code and prototypes for statistical modeling and graphics, but producing production-quality, well-integrated software applications typically requires human software-engineering expertise.

Develop and test experimental designs, sampling techniques, and analytical methods.

AI: Partial - AI can propose and simulate experimental designs and sampling strategies, but developing and thoroughly testing methods for complex, real-world experiments still needs human oversight and domain knowledge.

Plan data collection methods for specific projects and determine the types and sizes of sample groups to be used.

AI: Partial - AI can calculate sample sizes and recommend collection methods, but planning project-specific logistics, ethical considerations, and contextual sampling decisions typically require human input.

Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.

AI: Partial - AI can run diagnostics and flag methodological issues, but nuanced evaluation of validity, applicability, and tradeoffs generally requires human expertise and judgement.

Design research projects that apply valid scientific techniques and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.

AI: Partial - AI can draft research designs using historical baselines and standard techniques, but fully ensuring uncompromised, context-aware, and ethically sound study designs requires human leadership and review.

Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.

AI: Partial - As of 2025 AI can generate slides, charts and deliver prepared or virtual presentations and handle scripted Q&A, but it lacks consistent real‑time interpersonal judgment and in‑person presence required for many live meetings.

Develop an understanding of fields to which statistical methods are to be applied to determine whether methods and results are appropriate.

AI: Partial - AI can ingest domain literature and recommend appropriate methods, but it still lacks full tacit domain understanding and contextual judgment to guarantee appropriateness across novel or high‑stakes fields.

Supervise and provide instructions for workers collecting and tabulating data.

AI: Partial - AI can create protocols, training materials, and monitor data pipelines to guide data collectors, but cannot fully replace human supervisors for on‑the‑ground management and personnel decisions.

Evaluate sources of information to determine any limitations, in terms of reliability or usability.

AI: Partial - AI can perform provenance checks, automated data‑quality assessments and flag limitations, yet nuanced judgments about source reliability and usability often still require human expertise.

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: Partial - AI can assist with exploring mathematical structures and suggesting novel approaches, but reliable, independent discovery of foundational new statistical theory remains primarily a human endeavor as of 2025.

Report results of statistical analyses in peer-reviewed papers and technical manuals.

AI: Partial - AI can draft, format and populate manuscripts and manuals from analyses, but authorship accountability, interpretation, and adherence to peer‑review norms require human oversight and validation.