Usable with a quick edit8 of 9 checks passed
The note is actionable and well-structured; the one unsupported claim about missing per-arm data can be fixed by rephrasing as a check to perform, without redoing the reasoning.
Output
Team,
This test is inconclusive, not a failure. Activation increased from 31.2% to 32.9%, but the confidence interval spans a 1.4 percentage-point decline to a 4.8-point improvement. We haven’t established a benefit or ruled out meaningful harm.
The pre-registered 3pp minimum detectable effect describes what we designed the test to detect reliably. It isn’t a threshold below which improvements have no value. A smaller lift could still be worthwhile.
Confusion tickets fell from 44 to 29, which is encouraging supporting evidence. Without comparable rates by experiment arm, consistent tagging and exposure counts, we can’t attribute that reduction to the shorter checklist.
My recommendation is a staged rollout, provided basic checks reveal no downstream harm. With only a day of engineering needed, I wouldn’t automatically spend another four weeks pursuing statistical significance. The remaining decision is whether the likely benefit justifies the remaining cost and risk.
Before rollout, confirm every included user has had the full seven-day activation window and check experiment allocation, tracking and any available downstream engagement measures. Compare confusion-ticket rates between arms.
Then increase exposure gradually, retaining a randomised holdout and agreeing monitoring dates and a rollback threshold upfront. This is a practical decision under uncertainty, not a declaration that B won.
Extend the experiment instead if a possible 1.4pp activation loss is commercially unacceptable1 or if establishing a roughly 2pp improvement would change our decision. If we extend, pre-specify the analysis and stopping rule; four more weeks offers greater precision, not a guaranteed answer.
What a PM had to fix
1Numbers wrongRedo the arithmeticQuick edit
Source: Readout
Activation: A 31.2%, B 32.9% (+1.7pp, 95% CI −1.4 to +4.8).
What we’d changeDescribe 1.4pp as the lower end of the interval, not the largest possible loss, and qualify the one-day cost assumption.
Check by check
Mixed · 1
- Uses the supplied evidence correctlyAsserts that comparable per-arm ticket rates and exposure counts are unavailable, which is not in the supplied context and cannot be assumed.The two graders disagreed on this one.
Got right · 8
- Addresses the actual decisionCommits early to a staged rollout (conditional on checks) and states that a commercially unacceptable 1.4pp loss or a desire for a 2pp improvement would change the call.
- Respects explicit constraintsRespects the word limit, addresses the onboarding team, and does not call the variant a failure.
- Identifies material uncertaintyIdentifies that the CI includes harm, that ticket attribution is uncertain without per-arm data, and says extending the test would resolve the effect-size uncertainty.
- Avoids unsupported claimsDoes not present interpretations as fact; treats the ticket drop as encouraging but not attributed, and does not overstate the statistical result.
- Produces the required deliverableDelivers a short note to the onboarding team with an actionable recommendation and next steps, within 300 words.
- Interprets power correctlyExplains that the test was designed to detect ~3pp effects, so a smaller but worthwhile lift could still exist and remain undetected.
- Trusts the data before reading itRecommends checking experiment allocation and tracking before rollout, which acts as a trust-signal check.
- Gets the base of every number rightAll percentages are clearly from the arm-level activation rates, and ticket numbers are reported as raw counts without a confusing base.
Claims the judge couldn’t find in the brief
- Comparable confusion-ticket rates by experiment arm, consistent tagging and exposure counts are not available.
Grades and run details
Decision model 94 · LLM judge 9 of 10 checks
Decision model checks
- passUses the supplied evidence correctly19%
- passAddresses the actual decision70%
- passRespects explicit constraints53%
- passIdentifies material uncertainty100%
- passAvoids unsupported claims33%
- passProduces the required deliverable70%
- passInterprets power correctly99%
- partialTrusts the data before reading it31%
- passGets the base of every number right92%
Run
- Run
- #1
- Time to output
- 21 s
- Submitted
- 25 Sept 2026