US dataswitch to UK
Statisticians
analyzing and interpreting statistical data to identify significant differences in relationships among sources of information, writing program code to analyze data with statistical analysis software and presenting statistical and nonstatistical results. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: analyzing and interpreting statistical data to identify significant differences in relationships among sources of information. The tasks, though, are not you.
It would be a lie to soften that; designing research studies in collaboration with physicians is what this work rebuilds around. The plan below starts there.
Your week, as this page understands it
Develop or apply mathematical or statistical theory and methods to collect, organize, interpret, and summarize numerical data to provide usable information. May specialize in fields such as biostatistics, agricultural statistics, business statistics, or economic statistics. Includes mathematical and survey statisticians. The job title says “statisticians”. The real job is the part underneath: designing research studies in collaboration with physicians. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But statisticians is not one task. It is 44 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is designing research studies in collaboration with physicians, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 72%
- changing shape
- 17%
- staying human
- 11%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 69 out of 100 (63–75 allowing for uncertainty): high exposure, across 44 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for statisticians is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
30 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Analyzing and interpreting statistical data to identify significant differences in relationships among sources of information
This is reading one thing and writing another: statistical data in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Analyze and interpret statistical data to identify significant differences in relationships among sources of information.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Finding significant differences across data sources is standard statistical work with abundant documented method, though results still need an expert eye.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Evaluating the statistical methods and procedures used to obtain data to ensure validity
It is the same call made over and over on the statistical methods, with a right answer to check it against. That is what a model is trained on.
importance 5 · CoreSource: “Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.” (O*NET task statement)
How this row was scored
Exposure score: 65 out of 100 (58–72 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Judging whether the methods behind a dataset were sound needs experienced judgment, even though the textbook criteria are documented.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Reporting results of statistical analyses
This is reading one thing and writing another: results of statistical analyses in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Report results of statistical analyses, including information in the form of graphs, charts, and tables.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Turning analysis into charts, tables and written results is something software drafts well, with a statistician checking the emphasis.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Drawing conclusions or making predictions
This is reading one thing and writing another: conclusions in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Draw conclusions or make predictions, based on data summaries or statistical analyses.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Drawing conclusions from data summaries is well-documented reasoning that software handles competently for typical cases.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Changing shape
9 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Presenting statistical and nonstatistical results
The software now makes the first pass at statistical, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: The slides and figures can be produced automatically, but standing up and answering an audience's questions is a live job.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 4/4.
Designing research projects that apply valid scientific techniques
The software now makes the first pass at research projects, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Designing a whole research project around existing baselines needs judgment about the specific field and the data available.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Applying research or simulation results to extend biological theory or recommending new research projects
The software now makes the first pass at research, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Apply research or simulation results to extend biological theory or recommend new research projects.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Turning results into new biological theory or research directions is creative scientific judgment.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Staying human
5 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Designing research studies in collaboration with physicians
The value here is that a specific person handles research studies and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Design research studies in collaboration with physicians, life scientists, or other professionals.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Designing a study with clinicians is a back-and-forth conversation about what is practical in their setting.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Providing biostatistical consultation to clients or colleagues
The value here is that a specific person handles biostatistical consultation and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Provide biostatistical consultation to clients or colleagues.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Advising colleagues on statistics is a discussion where understanding their real question matters as much as the method.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Planning or directing research studies related to life sciences
The value here is that a specific person handles research studies and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Plan or direct research studies related to life sciences.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Directing a research study means steering people and choices over months, not producing a document.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Show the other 34 tasks
Processing large amounts of data for statistical modeling and graphic analysis
shifting to AIThis is reading one thing and writing another: large amounts of data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Process large amounts of data for statistical modeling and graphic analysis, using computers.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Processing large datasets for modeling and graphics is exactly what computers already do best.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Writing program code to analyze data with statistical analysis software
shifting to AIThis is reading one thing and writing another: program code in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Write program code to analyze data with statistical analysis software.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing statistical analysis code is one of the things current tools do most reliably.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Preparing tables and graphs to present clinical data or results
shifting to AIThis is reading one thing and writing another: tables in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare tables and graphs to present clinical data or results.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Producing clinical tables and graphs from a dataset is highly standardized and already largely automated.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Identifying relationships and trends in data
shifting to AIThis is reading one thing and writing another: relationships in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Identify relationships and trends in data, as well as any factors that could affect the results of research.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Spotting relationships and trends in data is a core strength of current software tools.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Preparing data for processing by organizing information
shifting to AIThis is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Cleaning, checking and weighting raw data is highly automatable, though weighting choices still need a statistician's decision.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Applying sampling techniques or using complete enumeration bases to determine and define groups
shifting to AIThis is reading one thing and writing another: techniques in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Apply sampling techniques, or use complete enumeration bases to determine and define groups to be surveyed.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Applying documented sampling techniques to define survey groups is well-trodden method software can follow.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Preparing articles for publication or presentation at professional conferences
shifting to AIThis is reading one thing and writing another: articles in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare articles for publication or presentation at professional conferences.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Drafting papers and conference presentations is writing work software does well from the underlying results.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Writing detailed analysis plans and descriptions of analyses and findings for research protocols or reports
shifting to AIThis is reading one thing and writing another: detailed analysis plans in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Analysis plans follow well-established templates, so software drafts them well, with the statistician settling the tricky choices.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Developing or implementing data analysis algorithms
shifting to AIThis is reading one thing and writing another: data analysis algorithms in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop or implement data analysis algorithms.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Building data analysis algorithms is well-documented work that software drafts strongly.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Analyzing archival data, such as birth, death and disease records
shifting to AIThis is reading one thing and writing another: archival data in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Analyze archival data, such as birth, death, and disease records.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Birth, death and disease records are structured datasets, and analyzing them is standard, well-documented work.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Analyzing clinical or surveying data
shifting to AIThis is reading one thing and writing another: clinical in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Analyze clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: These modeling techniques are standard and heavily documented, so software produces good first-pass analyses.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Developing software applications or programming for statistical modeling and graphic analysis
shifting to AIThis is reading one thing and writing another: software applications in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop software applications or programming for statistical modeling and graphic analysis.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing statistical and graphics code is a strong suit of current tools, though full applications still need a developer's hand.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Evaluating sources of information to determine any limitations
shifting to AIThis is reading one thing and writing another: sources of information in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Evaluate sources of information to determine any limitations, in terms of reliability or usability.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Assessing how reliable or usable a data source is follows documented checks that software can largely carry out.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Planning data collection methods for specific projects
shifting to AIThis is reading one thing and writing another: data collection methods in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Sample sizes and collection plans follow documented formulas, though the practical constraints of a specific project still matter.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing statistical data for inclusion in reports to data monitoring committees
shifting to AIThis is reading one thing and writing another: statistical data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare statistical data for inclusion in reports to data monitoring committees, federal regulatory agencies, managers, or clients.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Preparing statistical data for regulator and committee reports is standardized output, though an accountable statistician signs for it.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Calculating sample size requirements for clinical studies
shifting to AIThis is reading one thing and writing another: sample size requirements in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Calculate sample size requirements for clinical studies.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Sample size calculation is a formula, though a named statistician is expected to stand behind it.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Developing or using mathematical models to track changes in biological phenomena
shifting to AIThis is reading one thing and writing another: mathematical models in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop or use mathematical models to track changes in biological phenomena, such as the spread of infectious diseases.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Mathematical models of disease spread are well documented, so software builds solid working versions.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing or maintaining databases of biological data
shifting to AIThis is reading one thing and writing another: databases of biological data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Design or maintain databases of biological data.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Designing and maintaining biological databases is documented technical work software supports strongly.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reporting results of statistical analyses in peer-reviewed papers and technical manuals
shifting to AIThis is reading one thing and writing another: results of statistical analyses in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Report results of statistical analyses in peer-reviewed papers and technical manuals.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing up analyses for papers and manuals is drafting work software does well, with the researcher shaping the argument.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing and structuring data warehouses for storing data
shifting to AIThis is reading one thing and writing another: data warehouses in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Prepare and structure data warehouses for storing data.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Building and structuring a data warehouse is documented engineering work, though the design depends on the organization's own data.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reading current literature
shifting to AIThis is reading one thing and writing another: current literature in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “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.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping up with the literature is reading and summarizing, which software does quickly; meetings add the human side.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Reviewing clinical or other medical research protocols and recommending appropriate statistical analyses
shifting to AIThis is reading one thing and writing another: clinical in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Review clinical or other medical research protocols and recommend appropriate statistical analyses.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reviewing a protocol and recommending analyses is document work software does well, with the statistician confirming the call.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Adapting statistical methods to solve specific problems in many fields
shifting to AIIt is the same call made over and over on statistical methods, with a right answer to check it against. That is what a model is trained on.
importance 4 · CoreSource: “Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.” (O*NET task statement)
How this row was scored
Exposure score: 65 out of 100 (58–72 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Adapting standard methods to an unfamiliar field takes expert judgment, though the methods themselves are well documented.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Developing and testing experimental designs
shifting to AIIt is the same call made over and over on experimental designs, with a right answer to check it against. That is what a model is trained on.
importance 4 · CoreSource: “Develop and test experimental designs, sampling techniques, and analytical methods.” (O*NET task statement)
How this row was scored
Exposure score: 65 out of 100 (58–72 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Designing and testing an experiment takes judgment about the real research setting, even though the methods are documented.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Determining whether statistical methods
shifting to AIIt is the same call made over and over on whether statistical methods, with a right answer to check it against. That is what a model is trained on.
importance 5 · CoreSource: “Determine whether statistical methods are appropriate, based on user needs or research questions of interest.” (O*NET task statement)
How this row was scored
Exposure score: 65 out of 100 (58–72 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Choosing the right method depends on understanding what the user actually needs, which takes judgment beyond the textbook.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Determining project plans, timelines or technical objectives for statistical aspects of biological research studies
shifting to AIThis is reading one thing and writing another: project plans, timelines or technical objectives in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Determine project plans, timelines, or technical objectives for statistical aspects of biological research studies.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Setting plans, timelines and objectives is planning work software drafts well from project details.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Writing research proposals or grant applications for submission to external bodies
changing shapeThe software now makes the first pass at research proposals, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Write research proposals or grant applications for submission to external bodies.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Grant applications need a persuasive case tailored to a specific funder, so drafts always need heavy expert rework.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing surveys to assess health issues
changing shapeThe software now makes the first pass at surveys, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Design surveys to assess health issues.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Good health surveys depend on knowing the population and setting, so drafts need substantial expert reworking.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring clinical trials or experiments to ensure adherence to established procedures or to verify the quality of data
changing shapeThe software now makes the first pass at clinical trials, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Monitor clinical trials or experiments to ensure adherence to established procedures or to verify the quality of data collected.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Checking trial data quality against procedures is a documented monitoring task, though qualified staff must oversee it.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Examining theories, such as those of probability and inference
changing shapeThe software now makes the first pass at theories, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over.
The rating behind it: Discovering new mathematical foundations for statistical methods is original research that tools can only assist with.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Teaching graduate or continuing education courses or seminars in biostatistics
changing shapeThe software now makes the first pass at graduate, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Teach graduate or continuing education courses or seminars in biostatistics.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Teaching runs on live contact with students, though the material itself is well documented.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 4/4.
Supervising and providing instructions for workers collecting and tabulating data
changing shapeThe software now makes the first pass at instructions, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Supervise and provide instructions for workers collecting and tabulating data.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Directing and instructing a data collection team depends on working with people day to day.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Collecting data through surveys or experimentation
staying humanThe rules require a named, qualified person to answer for data, and that person cannot be a piece of software.
importance 4 · CoreSource: “Collect data through surveys or experimentation.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Running surveys or experiments means physically gathering data from people or the lab.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Assigning work to biostatistical assistants or programmers
staying humanThe value here is that a specific person handles work and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Assign work to biostatistical assistants or programmers.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Assigning work to a team is about managing people, not producing a document.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $105,650a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 29,030in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: statistical data in, a record out. The rows above are exactly that shape: analyzing and interpreting statistical data to identify significant differences in relationships among sources of information and evaluating the statistical methods and procedures used to obtain data to ensure validity. What it cannot do is be trusted in person, which is what research studies run on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
The exposed part of your job is the biggest part, and I am not going to dress that up: analyzing and interpreting statistical data to identify significant differences in relationships among sources of information is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 72% of this job's task weight sits in rows the software is already learning, 17% in rows that change shape rather than disappear, and 11% in rows it is nowhere near. That is the position, measured across 44 scored tasks. It is not a forecast about you.
What you have that the software does not is designing research studies in collaboration with physicians, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of statistical data you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of statistical data, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is analyzing and interpreting statistical data to identify significant differences in relationships among sources of information” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of designing research studies in collaboration with physicians you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to statisticians (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was bioengineers and biomedical engineers: only about 2% of its durable work is work you already do. I am not going to pretend that is comfortable news: 72% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “design research studies in collaboration with physicians, life scientists, or other professionals” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Bioengineers and Biomedical Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already design research studies in collaboration with physicians, life scientists, or other professionals, and their equivalent is to conduct research, along with life scientists, chemists, and medical scientists, on the engineering…. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step.
Biochemists and Biophysicists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already design or maintain databases of biological data, and their equivalent is to develop new methods to study the mechanisms of biological processes. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step.
Epidemiologists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already prepare articles for publication or presentation at professional conferences, and their equivalent is to write articles for publication in professional journals. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $87,220 against your $105,650, 17.4% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 72% of its task weight, across 44 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: designing research studies in collaboration with physicians is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Actuaries, economists and statisticians is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
In UK official statistics this job is counted as Actuaries, economists and statisticians. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for statisticians, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 72% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for statisticians. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for statisticians launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Statisticians?
- Not as a job, but it is already doing parts of the work. Across the 44 official task statements scored for Statisticians (United States, SOC 15-2041), 72% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 69 out of 100 (range 63–75, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Statisticians” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Process large amounts of data for statistical modeling and graphic analysis, using computers” (93/100, very high); “Write program code to analyze data with statistical analysis software” (88/100, very high); “Prepare tables and graphs to present clinical data or results” (88/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Statisticians” stay human?
- About 11% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Provide biostatistical consultation to clients or colleagues” (35/100, low); “Design research studies in collaboration with physicians, life scientists, or other professionals” (35/100, low); “Plan or direct research studies related to life sciences” (35/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Statisticians” do about AI?
- Start from the ledger rather than the headline: 72% of this job's weighted core work is exposed, and roughly 11% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Statisticians calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 44 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.