Draw conclusions or make predictions, based on data summaries or statistical analyses.

“Draw conclusions or make predictions, based on data summaries or statistical analyses.” is a core task performed by Biostatisticians. Among the occupation's 25 rated tasks, workers place it 25th by importance (#1 most important). About 100% 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.

• Analyze scientific or applied data using mathematical principles. in Analyzing Data or Information

AI exposure

The OpenAI / Eloundou “GPTs are GPTs” study rates this task E2. Exposure with tools — software built on top of a language model (not the model alone) could cut the time 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 0.50. Automation potential label: T3.

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.

• 0.084% share of AI-use records mapped to this task
• 19% of that use is work-related
• Most common interaction: directive
• 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 clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques. · importance 4.4
• Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports. · importance 4.4
• Calculate sample size requirements for clinical studies. · importance 4.3
• 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. · importance 4.3
• Prepare tables and graphs to present clinical data or results. · importance 4.3
• Design research studies in collaboration with physicians, life scientists, or other professionals. · importance 4.3
• Write program code to analyze data with statistical analysis software. · importance 4.3
• Provide biostatistical consultation to clients or colleagues. · importance 4.3
• Review clinical or other medical research protocols and recommend appropriate statistical analyses. · importance 4.2
• Prepare statistical data for inclusion in reports to data monitoring committees, federal regulatory agencies, managers, or clients. · importance 4.2
• Determine project plans, timelines, or technical objectives for statistical aspects of biological research studies. · importance 4.2
• Develop or implement data analysis algorithms. · importance 4.2
• Plan or direct research studies related to life sciences. · importance 4.0
• Prepare articles for publication or presentation at professional conferences. · importance 4.0

See all tasks on the Biostatisticians 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-16254,
  title  = {Draw conclusions or make predictions, based on data summaries or statistical analyses.},
  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-16254}
}