Understanding Your Results
This guide explains how to use AudiAInce results without confusing model-generated output with evidence from people or live markets.
Start With the Evidence Label
AudiAInce standard audience simulations compare responses across 19 structured AI audience archetypes. Unless a report explicitly includes a separate human or live-market dataset, the displayed responses come from models, not respondents.
| Display | What It Describes | Appropriate Use | | --- | --- | --- | | Model score | A model-generated rating under the supplied context | Compare hypotheses inside the same run | | Archetype distribution | Variation across defined AI audience archetypes | Explore differing perspectives | | Model-simulation range | Mathematical or model variation | Notice sensitivity inside the simulation | | Reference comparison | Model output beside a cited external statistic | Check alignment or divergence on that question | | Suggested action | Model-generated recommendation | A candidate to review and validate |
None of these, alone, is a human-population estimate or prediction guarantee.
Comparing Options
When comparing A and B:
- look at the direction and size of the model-generated difference
- review whether the pattern is broad or concentrated in a few archetypes
- read the objections and qualitative reasoning
- rerun with plausible alternative assumptions or wording
- validate the leading hypothesis with real customers or a live test
Do not describe a model-only difference as population-level statistical evidence about people.
Archetype and Segment Analysis
Displayed segments may represent individual archetypes or groupings of similar model responses. They can help you explore different reactions, but they are not measured market segments.
Useful Questions
- Which defined attributes appear alongside the reaction?
- What reason or objection produced the score?
- Does the theme repeat across several archetypes?
- What customer evidence could confirm or reject it?
Segment proportions in a model-only report should not be treated as population shares.
Response Distributions
Charts show the shape of model-generated responses. A tight distribution can mean the model produced similar outputs under the run's assumptions; a wide or divided distribution can signal sensitivity. Neither proves agreement or disagreement among real customers.
Open-Ended Responses
AI-generated themes and quotations can help draft hypotheses, copy variants, or interview questions. They are simulated language and must not be attributed to customers unless the report separately identifies a real, consented source.
Common Misinterpretations
“Most respondents said they would buy”
Issue: The standard simulation uses AI archetypes, not human respondents. Stated model intent is not a conversion forecast.
Better wording: “Most model-generated archetype responses favored this option; validate purchase behavior in market.”
“Option A is proven to beat Option B”
Issue: A model-only ranking does not prove a human-population lift.
Better next step: Use the ranking to choose a candidate for a properly designed live or human test.
“This segment represents our market”
Issue: Archetype or cluster proportions are not measured market shares.
Better next step: Compare the hypothesis with first-party data or recruited research from the relevant population.
“The reference match validates the whole report”
Issue: Agreement on one statistic does not establish accuracy for other questions or outcomes.
Better next step: Review the cited methodology and validate each consequential hypothesis with suitable evidence.
Taking Action
Use the output to prioritize what to test next when:
- the model-generated difference is material enough to investigate
- the reasoning is coherent and relevant to the product
- the result is not driven by an implausible assumption
- the proposed test is reversible and proportionate to the evidence
Require stronger real-world evidence when a decision is expensive, difficult to reverse, regulated, or consequential to people.
Related Articles
- Model Diagnostics and Statistical Methods
- Calibration - Understanding reference comparisons
- Surveys - Running survey-style simulations
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