{ "source": "Cooked Index — occupational AI risk register", "page": "https://cookedindex.com/jobs/statistical-assistants/", "methodology": "https://cookedindex.com/methodology", "notice": "Verdicts are re-examined as evidence accumulates. Re-fetch before relying on this; the page above always carries the current score.", "scored_at": "2026-08-11", "model": "claude-opus-5", "occupation": { "title": "Statistical Assistants", "soc_code": "43-9111", "category": "Office", "us_employment": 4710, "median_annual_wage": 50330 }, "verdict": "COOKED", "risk_resistance": 14, "contested": false, "near_boundary": false, "dimensions": { "task_resistance": 4, "embodiment": 2, "liability_shield": 1, "trust_premium": 3, "judgment_accountability": 4 }, "reasoning": { "task_resistance": "Recoding open-ended survey responses into category codes, deduplicating respondent records, running the prescribed regression or weighting procedure in SAS/R/SPSS, and formatting the output into tables are each tasks an LLM plus a scripting pass now completes end-to-end; the 4 rather than 0 reflects the residual chasing of missing questionnaires, reconciling a field office's spreadsheet against the master file, and noticing that a variable's coding changed between collection waves.", "embodiment": "The only physical elements are retrieving paper questionnaires or archived record files and occasionally operating scanning or data-entry equipment; everything else happens at one workstation, which is why this sits at 2 rather than 0.", "liability_shield": "No state licence, no certification exam, no signature block — an ASA accreditation exists for statisticians and is neither required nor typically held at assistant level, and any error you introduce into a dataset is caught and owned by the statistician who signs the report.", "trust_premium": "Your outputs reach internal researchers, program staff, or a supervising statistician who judge the tables on whether the numbers reconcile, not on who produced them; the 3 covers the practical fact that a colleague who knows your files trusts your cleaning conventions and will ask you rather than re-derive them.", "judgment_accountability": "Codebooks, survey specifications, and imputation rules set in advance dictate almost every call you make; the discretion at 4 is deciding whether a suspicious outlier is a real value or a keying error, and whether a response rate is low enough to flag upward — decisions someone else then ratifies." }, "rationale": "The job is compiling data from surveys and records, cleaning and coding it, running prescribed statistical computations, and building tables and charts for someone else to interpret — which is precisely the work current AI plus modern scripting already does at usable quality. There is no license, no signature, and no client relationship; the statistician or researcher above you owns the interpretation and the accountability. The surviving fragment is data acquisition and quality judgment in messy institutional settings — knowing which source is trustworthy, why a field is broken, how a survey instrument distorts responses.", "outlook": "Headcount in this small occupation keeps shrinking as analysts run their own AI-assisted tooling; the remaining roles convert into junior data engineer and data quality positions with different titles and higher credential requirements.", "what_would_raise_it": { "levers": [ { "dimension": "task_resistance", "change": "Task-mix shift: if the scripted tier (recode, merge, tabulate, chart) is fully absorbed, what remains is data provenance and defect diagnosis — reconciling conflicting administrative sources, spotting instrument-induced response distortion, deciding whether a broken field is imputable. This tier genuinely exists in survey shops (Census, NORC, RTI) and in clinical data management, and it is what audit trails actually ask about. Watch for job postings retitled 'data quality analyst' or 'data manager' with the same pay band.", "plausibility": "already happening", "would_add": 4 }, { "dimension": "liability_shield", "change": "Extension of FDA 21 CFR Part 11 electronic-records practice — where a named individual's e-signature attaches to each data query resolution and database lock — into AI-produced derivations. FDA's January 2025 draft guidance on AI in regulatory decision-making asks sponsors to document human credibility assessment of model outputs; if that hardens into a requirement that a named data manager attests to AI-generated dataset transformations, the clinical-trial slice of this SOC acquires a real signature.", "plausibility": "plausible", "would_add": 4 }, { "dimension": "liability_shield", "change": "Model-risk validation regimes (Fed/OCC SR 11-7, and its spread to insurance via NAIC's 2023 AI model bulletin adopted in 20+ states) requiring a named human to document input-data lineage and quality for any model used in pricing or underwriting. That documentation work is exactly this occupation's residual, and SR 11-7 already demands independent, attributable review.", "plausibility": "plausible", "would_add": 3 }, { "dimension": "judgment_accountability", "change": "Formal ownership of the disclosure-avoidance and data-suppression call. Census's differential-privacy rollout and state health-department small-cell suppression rules put a consequential, ambiguous decision (release or suppress) on whoever prepares the table. If agencies name that person in the release documentation rather than treating it as clerical, the role owns a real call.", "plausibility": "plausible", "would_add": 3 }, { "dimension": "judgment_accountability", "change": "Sworn-agent status under Title 13 (Census) and similar confidentiality oaths carry criminal penalty for improper disclosure and already attach personally. Broader use of sworn special-agent designations for contractor data staff handling linked administrative records would make the disclosure judgment personally consequential.", "plausibility": "plausible", "would_add": 2 } ], "ceiling_note": "Even with every lever, this caps in the high 20s–low 30s. There is no license, no client who chose you, and no physical component — and the liability levers attach to specific sectors (clinical trials, regulated finance, federal statistics) that hold well under half of these 4,710 workers. For the rest, compiling and charting inside a general office, no plausible route exists; trust_premium in particular has none, because the buyer of a cleaned dataset has never known or cared who cleaned it." }, "adjudication": null, "employment_history": { "points": [ { "y": 2017, "emp": 10110, "wage": 47400 }, { "y": 2018, "emp": 11010, "wage": 48330 }, { "y": 2019, "emp": 9810, "wage": 49870 }, { "y": 2020, "emp": 9320, "wage": 50360 }, { "y": 2021, "emp": 6190, "wage": 48160 }, { "y": 2022, "emp": 6710, "wage": 48880 }, { "y": 2023, "emp": 7200, "wage": 50510 }, { "y": 2024, "emp": 5900, "wage": 51440 }, { "y": 2025, "emp": 4710, "wage": 50330 } ], "from": 2017, "to": 2025, "change_pct": -53.4, "comparable_from": 2019, "spans_soc_revision": true }, "pivots": [ { "slug": "financial-specialists-all-other", "title": "Financial Specialists, All Other", "verdict": "EXPOSED", "risk_resistance": 36, "median_wage": 81100, "overlap": 73, "skills_to_close": [ "Management of Financial Resources", "Management of Material Resources", "Judgment and Decision Making", "Operations Monitoring" ] }, { "slug": "data-scientists", "title": "Data Scientists", "verdict": "EXPOSED", "risk_resistance": 37, "median_wage": 120230, "overlap": 73, "skills_to_close": [ "Systems Analysis", "Systems Evaluation", "Operation and Control", "Operations Monitoring" ] }, { "slug": "economists", "title": "Economists", "verdict": "EXPOSED", "risk_resistance": 35, "median_wage": 124720, "overlap": 72, "skills_to_close": [ "Management of Financial Resources", "Systems Analysis", "Systems Evaluation", "Active Listening" ] } ], "license": "https://cookedindex.com/terms" }