Usable with a quick edit8 of 9 checks passed
The core reasoning and use of evidence are sound, and the only meaningful omissions are explicit trust checks and the original 3pp MDE, which could be added in a quick edit. No rechecking or reanalysis is required.
Output
Team—
This is inconclusive, not a failure. Activation was 1.7 percentage points higher with the shorter checklist, but the 95% CI (−1.4 to +4.8 points) includes no effect and possible harm—as well as a benefit of at least 3 points. The test wasn’t precise enough to settle the question.
I recommend running the test for another four weeks. That should let us assess effects around 2 points, which this change may plausibly deliver. Before extending, agree on what activation gain would justify rollout and what activation decline would stop it. Track onboarding-confusion tickets too, but compare rates per user: the raw drop from 44 to 29 is encouraging, not conclusive.
After the extension, decide using both activation and confusion—not a “significant/not significant” label alone.
Check by check
Got wrong · 1
- Trusts the data before reading itIt does not explicitly check any trust signal such as sample ratio, exposure or logging quality, or novelty before interpreting the activation result.
Got right · 8
- Uses the supplied evidence correctlyEvery stated fact or figure is drawn correctly from the supplied readout/options, and no current-situation facts are invented.
- Addresses the actual decisionIt unambiguously answers that the result is inconclusive, not a failure, and says the next step is a four-week extension with pre-agreed thresholds before deciding.
- Respects explicit constraintsThe output is a note to the onboarding team, stays well under 300 words, and respects the requested short-note form.
- Identifies material uncertaintyIt names the unresolved true activation effect and support-ticket rate issue, bounds the activation uncertainty with the CI, and says extension plus pre-agreed thresholds would resolve it.
- Avoids unsupported claimsInterpretive claims such as 'not precise enough' and 'encouraging, not conclusive' are appropriately hedged and follow from the supplied data.
- Produces the required deliverableThe requested note is present, actionable, and complete enough for the onboarding team to proceed.
- Interprets power correctlyIt communicates that the original test could not distinguish the observed 1.7pp effect from no effect or harm, and that more data would allow detection around 2pp.
- Gets the base of every number rightThe activation figures and differences are computed from the right values, and the output flags that the ticket drop should be compared per user rather than as a raw count.
Grades and run details
Decision model 89 · LLM judge 9 of 10 checks
Decision model checks
- passUses the supplied evidence correctly79%
- passAddresses the actual decision97%
- passRespects explicit constraints95%
- passIdentifies material uncertainty98%
- passAvoids unsupported claims47%
- passProduces the required deliverable66%
- passInterprets power correctly86%
- failTrusts the data before reading it79%
- passGets the base of every number right95%
Run
- Run
- #1
- API response time
- 9 s
- Submitted
- 29 Sept 2026