Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical 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.” is a core task performed by Statisticians. Among the occupation's 19 rated tasks, workers place it 3rd by importance (#17 most important). About 90% of workers say it is relevant to their job.

This is a single occupation-specific task statement from O*NET. The figures below describe how central the task is to the job and what independent studies measure about AI and this kind of work — not a prediction that the task will be automated.

Work activities this task rolls up to

O*NET groups concrete tasks into broader work activities shared across many occupations.

• Maintain current knowledge in area of expertise. in Updating and Using Relevant Knowledge

AI exposure

The OpenAI / Eloundou “GPTs are GPTs” study rates this task E1. Direct exposure — a language model could plausibly cut the time to do this task by at least half.

Exposure measures whether a model could meaningfully speed the task up — it is an estimate of overlap with model capabilities, not a measure of whether the work will be done by software. The study's intermediate score (β) for this task is 1.00. Automation potential label: T1.

How AI is actually used on this kind of task

The Anthropic Economic Index observes how people actually use AI on tasks like this one across millions of real conversations.

• 0.100% share of AI-use records mapped to this task
• Most common interaction: learning
• Average autonomy of the AI: 3.4 (1–5; higher = more autonomous)
• 88% of interactions still needed a human in the loop

Observed AI use describes people choosing to use AI as a tool on this kind of task today. It is augmentation and assistance, not a measure of jobs replaced.

Working with AI vs. handing it off

Of the AI conversations mapped to this task, the split between people working alongside AI and people delegating the task to it.

How people interact with AI on this task

Other tasks in this occupation

• Analyze and interpret statistical data to identify significant differences in relationships among sources of information. · importance 4.7
• Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy. · importance 4.7
• Report results of statistical analyses, including information in the form of graphs, charts, and tables. · importance 4.5
• Determine whether statistical methods are appropriate, based on user needs or research questions of interest. · importance 4.5
• Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data. · importance 4.5
• Develop and test experimental designs, sampling techniques, and analytical methods. · importance 4.4
• Identify relationships and trends in data, as well as any factors that could affect the results of research. · importance 4.3
• Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students. · importance 4.3
• Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses. · importance 4.3
• Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering. · importance 4.3
• Evaluate sources of information to determine any limitations, in terms of reliability or usability. · importance 4.1
• Process large amounts of data for statistical modeling and graphic analysis, using computers. · importance 4.0
• Develop software applications or programming for statistical modeling and graphic analysis. · importance 3.9
• Report results of statistical analyses in peer-reviewed papers and technical manuals. · importance 3.9

See all tasks on the Statisticians page.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-task-8967,
  title  = {Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
  url    = {https://singulariki.com/tasks/task-8967}
}