---
interpretation: lens
---
**Mandate:** The agent **MUST** verify the user-research methodology produces reliable, defensible insights. Research that confirms what the team already believed, or that generalizes from a thin sample, ships strategy that breaks on contact with real users. The methodology lens catches that before prioritization scores against it.

## Check

The agent **MUST** verify, filing feedback for any violation:

- **Question quality** — Research questions are well-formed, answerable, and could return "no." Loaded questions ("how much do users love X?") are findings to file.
- **Sample representativeness** — The sample reflects the segments named in the unit's framing. Thin or biased samples are caveated explicitly and flagged as hypotheses, not findings.
- **Method-to-question fit** — Qualitative methods are used for depth questions, quantitative for breadth. A 4-person survey is not a quantitative finding; a 200-person interview reduction is not qualitative depth.
- **Pattern strength** — Insights are derived from patterns across at least three independent signals, not single anecdotes. Counter-signals are named for every theme.
- **Behavior over stated preference** — Where users' behavior and stated preference diverge, both are captured and the divergence is named, not silently resolved in favor of one.
- **Jobs-to-be-done in user language** — Jobs-to-be-done are written in the user's words, framed in the standard `When I [situation], I want to [motivation], so I can [outcome]` shape. Product-feature language ("users want our search to be faster") is a finding to file.
- **Segment differences preserved** — Where the same job-to-be-done shows up differently across segments, the difference is preserved as a named tension, not averaged away.

## Common failure modes to look for

- Insights stated as facts with no supporting participant IDs, survey responses, or analytic events
- A single loud voice elevated above representative patterns
- Survey response rates omitted or response rates below 10% on self-selected lists treated as representative
- Themes with three supporting signals but no counter-signals — confirmation bias signature
- Jobs-to-be-done that read like feature requests
- Cross-segment averaging that hides a tension the prioritization stage needs to see
