Research report · AskYourMarket · September 9, 2026
Executive Summary
We ran a controlled benchmark comparing 100 AskYourMarket synthetic respondents against Pew Research Center's nationally representative survey of 5,119 U.S. adults on public attitudes toward AI (fielded February 2026). This test is part of an ongoing effort to answer the question every serious buyer of synthetic market research eventually asks: does synthetic data in market research actually hold up against real people? Using identical question wording and identical answer options, our synthetic respondents correctly identified the leading, most common answer in 4 of 5 questions (80% directional accuracy). On individual response options, results landed within an average of 7.5 percentage points of real-world data — improved from 12 points before we wired real government statistics into our generation pipeline. On one question, synthetic and real results matched within 1-3 points across every single answer option.
What Is Synthetic Data in Market Research?
Before the results, a quick grounding for readers new to this space: synthetic data in market research refers to AI-generated respondent profiles and answers, built to simulate how a real target audience might react to a product, price, or message used for fast, low-cost early-stage testing before committing budget to real fieldwork. It's not a replacement for primary research; it's a screening tool, closer to an AI focus group you can run in minutes than a statistically certified survey.
Methodology
We used Pew Research Center's exact published question wording and answer options, not our own paraphrasing, to remove any ambiguity from the comparison. AskYourMarket generated a synthetic population of 100 respondents matching Pew's target demographic (U.S. adults, general population), then answered all 5 questions using our existing single-pass persona-consistency method. Critically, this run used AskYourMarket's calibration system: rather than letting the underlying AI model freely estimate demographic distributions and behavior patterns, we grounded population generation and select response patterns in verified statistics from Pew Research Center, the U.S. Census Bureau, and the Federal Reserve Board — the same public institutions we're benchmarking against. The AI model's role narrows to generating plausible individual reasoning and voice on top of a demographically real foundation, rather than inventing the foundation itself.
Results, Question by Question
- Q1 — Is AI advancing too quickly? Real: 63% too quickly. Synthetic: 69%. A 6-point gap on the leading answer, with the biggest structural improvement showing up in a category real researchers watch closely: uncertainty. Before calibration, our synthetic respondents almost never said "not sure" — a clear tell of an ungrounded model. After calibration, "not sure" responses appeared at a realistic ~13% rate, closely tracking Pew's own 16%.
- Q2 — Will AI make personal information less secure? Real: 71% less secure. Synthetic: 80%. Same directional match, same meaningful reduction in the "always confident, never uncertain" pattern that flagged our first attempt as ungrounded.
- Q3 — Do you use AI chatbots? Real: an almost even split (51% no, 49% yes). Synthetic: 73% yes. This is our clearest miss, and the most useful one — the real population is genuinely divided on this question, while our synthetic population overweighted adoption. We've traced this to a specific gap in how our calibration system currently applies demographic-linked anchors to this exact question type, and it's our next fix, not a mystery.
- Q4 — What will AI's societal impact be over 20 years? Real: 31% say equally positive and negative. Synthetic: 60%. Directionally correct — both point to "mixed outlook" as the dominant view — but synthetic responses converged toward that middle answer more strongly than real humans did.
- Q5 — Do you read AI search summaries? Real: 60% yes. Synthetic: 59% yes. Our tightest result — every answer option landed within 1-3 percentage points of the real distribution.
What This Tells Us About Synthetic Respondents
Synthetic research isn't uniformly accurate or uniformly wrong — it's accurate on some question types and needs more work on others, and the honest, useful thing to do is show exactly which is which. Attitude and opinion questions (Q1, Q2, Q5) calibrated tightly. A behavioral adoption question with a near-even real-world split (Q3) exposed a specific, fixable gap in how our system currently applies demographic calibration to synthetic respondents. That's not a reason to distrust the tool — it's the reason benchmark testing exists, and it's why we're publishing this comparison instead of a marketing claim.
Limitations
All respondents are synthetic (AI-generated) and were not real people. Results are directional, intended for early-stage exploration and hypothesis generation — not a replacement for primary research before high-stakes decisions. This benchmark reflects one snapshot of an actively evolving calibration system; we'll publish updated comparisons as accuracy improves.
Full raw dataset and question-by-question breakdown available via CSV/Excel export. Source data: Pew Research Center, "Americans and AI 2026" (pewresearch.org/internet/2026/06/17/americans-and-ai-2026-chatbots-smart-devices-and-views-on-impact/).