The verified, real-world data that a model or analysis is measured against. In UX research: actual behavior, actual interviews, actual outcomes, not what a model predicts.
Definition: The verified, real-world data that a model or analysis is measured against. In UX research: actual behavior, actual interviews, actual outcomes, not what a model predicts.
The verified, real-world data your model or analysis is measured against. In UX research that means actual user behavior, actual interviews, actual outcomes, not what a model predicts they would be. Without ground truth you cannot tell whether an AI tool is working or just sounding confident. (Yes, this is also where the company tagline comes from.)
AI-generated personas marketed as substitutes for real research participants. The pitch is faster and cheaper than recruitment; the reality is regression to training-data averages dressed up as user voices.
When an AI model generates plausible-sounding but factually incorrect or fabricated information. A natural consequence of how LLMs predict probable text rather than verify truth.
Systematic deviation from the true value in research findings. Cannot be eliminated, only managed through standardization and awareness. The goal is systematic bias (manageable) over unsystematic bias (chaos).