Tasks / Experiment

Analyse experiment results

Can the model separate evidence from speculation, identify decision-relevant uncertainty and recommend a sensible next action?

Measures the modelTask v1.2 · 2 casesDifficulty

What AI gets right here, and what you’ll still have to catch

From 14 graded outputs by 7 models. 71% were usable with at most a quick edit.

Reliably right

  1. Gets the base of every number right100% pass
    The refund increase and revenue difference are computed correctly, and the retention CI interpretation uses the correct 2pp threshold.
    GPT-6 Astra · ChatGPT · Conversion up, retention down
  2. Interprets power correctly100% pass
    Explains that the test was designed to detect ~3pp effects, so a smaller but worthwhile lift could still exist and remain undetected.
    GPT-6 Astra · ChatGPT · The underpowered onboarding test
  3. Checks guardrails before declaring a winner100% pass
    It evaluates both the day-30 retention guardrail and the refund-request guardrail before making a call.
    GPT-6 Astra · ChatGPT · Conversion up, retention down

Where it slips

  1. Trusts the data before reading it21% pass
    It does not explicitly check a pre-interpretation trust signal such as sample ratio or logging before relying on the lift; the denominator and maturity caveats come after interpreting.
    GPT-6.1 Sol · API · Conversion up, retention down
  2. Avoids unsupported claims52% pass
    It asserts the refund surge 'isn't noise' and that annual up-front billing means retention 'should rise' without supporting evidence or clear labeling.
    Sonnet 5.5 · API · Conversion up, retention down
  3. Uses the supplied evidence correctly55% pass
    Asserts that comparable per-arm ticket rates and exposure counts are unavailable, which is not in the supplied context and cannot be assumed.
    GPT-6 Astra · ChatGPT · The underpowered onboarding test

Case viewer

Read the brief, then put up to three outputs side by side, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

Our new pricing page variant (B) increased trial-to-paid conversion, and the growth team wants to ship it to 100% on Monday. Review the readout below and write your recommendation to Priya Nair, Head of Growth, who makes the call. Lead with what we should do, then give the reasoning she needs to defend it in the Monday meeting. Keep it under 400 words.

What the model was given5 items: Scenario, Readout summary, Variant description, Guardrails agreed before launch, Refunds
ScenarioLumen is a subscription app for learning languages. The monthly plan is $14.99. The annual plan is $119.99, billed up front, which works out at $9.99 a month.
Readout summaryTest ran 21 days, 50/50 split, 38,412 trial starts. Trial→paid conversion: A 11.8%, B 13.1% (+1.3pp, 95% CI +0.6 to +2.0). Day-30 paid retention: A 84.0%, B 79.5% (−4.5pp, 95% CI −7.9 to −1.1). Revenue per trial start at day 30: A $9.42, B $9.61 (CI crosses zero).
Variant descriptionVariant B leads with the annual plan's monthly-equivalent price ($9.99/month) and moves the monthly plan behind a 'See all plans' link.
Guardrails agreed before launchDay-30 retention must not fall more than 2pp. Refund requests must not rise more than 10%.
RefundsRefund requests during the test: A 212, B 301 (+42%).
What a strong answer doesThe answer key the graders mark against

Do not ship B to 100%. Conversion gain is real but guardrails are breached; the likely mechanism is buyers misreading the monthly-equivalent price. Recommend a follow-up variant that keeps the clarity gain without the misleading anchor, and quantify the revenue picture honestly.

Critical failures (cap the score at 40)
  • Recommends shipping B to 100%
  • States the mechanism as established fact
Case

Mid-level · v1.9 · anonymised real · pricing, guardrail breach, B2C SaaS

The outputs

7 setups answered this brief. Pick up to three to read side by side.

Critical failure: States the mechanism as established fact. Score capped at 40.

Needs more than a quick edit2 of 10 checks passed

A PM would need to remove unsupported causal and forecast claims and add decision-changing conditions. It also needs addressing to Priya before use.

Output

Recommendation: Do not ship Variant B. Keep Variant A live and do not roll B out to 100% on Monday.

While Variant B delivered an initial +1.3pp lift in trial-to-paid conversion, it violates both pre-agreed guardrails, creates bill shock, and delivers no statistically significant revenue upside.

Here is the reasoning to defend this decision on Monday:

1. Both Pre-Agreed Guardrails Were Blown

We agreed on strict risk thresholds before launch. Variant B failed both: * Day-30 Retention: Fell −4.5pp (down to 79.5%), more than double our maximum allowed drop of 2pp (95% CI: −7.9 to −1.1). * Refund Requests: Surged +42% (301 vs. 212), four times our 10% ceiling.

Overriding guardrails after the fact sets a dangerous precedent and invites compounding churn.

2. The Conversion Lift Is an Illusion Driven by Bill Shock

Variant B hides the monthly tier and frames the annual plan as “$9.99/month.” Users clearly believe they are committing to a flexible monthly rate, only to be charged $119.99 upfront. The 42% spike in refunds and immediate drop in 30-day retention prove that these are not high-intent, qualified subscribers—they are confused customers who churn or dispute the charge as soon as they review their statements.

3. Long-Term Economics Are Flat to Negative

  • Day-30 revenue per trial start is statistically unchanged: $9.61 for B vs. $9.42 for A (CI crosses zero).
  • When factored against the operational cost of processing 42% more refunds, payment dispute fees, customer support load, and downstream LTV decay from lower retention, Variant B will be net-negative over a 12-month horizon.
  • App store ratings and brand trust will also suffer if users perceive our checkout flow as deceptive.

Next Step

Iterate on Variant C: keep the focus on annual plan value, but make the upfront billing explicit (e.g., “$9.99/mo, billed annually at $119.99”) and test clear annual vs. monthly side-by-side positioning rather than hiding the monthly option.

Claims the judge couldn’t find in the brief

  • Users clearly believe they are committing to a flexible monthly rate, only to be charged $119.99 upfront.
  • The 42% refund spike and immediate drop in 30-day retention prove these are not high-intent, qualified subscribers but confused customers who churn or dispute the charge as soon as they review statements.
  • Variant B will be net-negative over a 12-month horizon after refund processing, dispute fees, support load, and LTV decay.
  • App store ratings and brand trust will suffer if users perceive the checkout flow as deceptive.

Grades and run details

Decision model 50 · LLM judge 2 of 11 checks
Decision model checks
  • failUses the supplied evidence correctly55%
  • passAddresses the actual decision55%
  • partialRespects explicit constraints22%
  • partialIdentifies material uncertainty32%
  • failAvoids unsupported claims90%
  • passProduces the required deliverable94%
  • passChecks guardrails before declaring a winner100%
  • failSeparates effect from explanation49%
  • failTrusts the data before reading it99%
  • passGets the base of every number right77%
Run
Run
#1
API response time
22 s
Submitted
1 Oct 2026
Critical failure: States the mechanism as established fact. Score capped at 40.

Usable with a quick edit4 of 10 checks passed

The core recommendation is sound, and the single problematic wording ('confirms') could be quickly edited to 'suggests' without redoing any reasoning or re-checking evidence.

Output

Hi Priya,

Recommendation: Do not ship Variant B to 100% on Monday. While it improves trial-to-paid conversion, it severely breaches our pre-agreed guardrails on both retention and refund requests, threatening long-term revenue health.

Here is the reasoning to defend holding off on Monday:

  • Guardrail Breach on Retention: We agreed in advance that Day-30 paid retention must not drop by more than 2 percentage points. Variant B’s retention dropped by 4.5pp1 (down to 79.5% from A's 84.0%, with a 95% CI of −7.9 to −1.1). This massive drop indicates we are converting users who aren’t genuinely committed to the subscription.2
  • Severe Spike in Refunds: Refund requests jumped by 42% (301 requests for B vs. 212 for A), completely shattering our guardrail limit that refunds must not rise by more than 10%. This confirms that Variant B’s presentation3—leading with the $9.99/month equivalent and hiding the monthly plan—creates subscriber confusion or misleading expectations at checkout.
  • Neutral Long-Term Revenue: Although Variant B boosts initial trial-to-paid conversion (+1.3pp) and shows a nominal lift in day-30 revenue per trial start ($9.61 vs. $9.42), the confidence interval crosses zero. Combined with the steep drop in retention and surge in refunds, the initial revenue gains are illusory4 and will likely erode over time.

Next Steps: Keep Variant A live. Instruct the growth team to iterate on the pricing presentation—addressing the confusion around the annual plan framing—without sacrificing user retention or triggering buyer remorse.

What a PM had to fix

  1. 1Contradiction missedSurface the contradictionSubstantial rework

    Source: Readout summaryTest ran 21 days

    What we’d changeFlag that a 21-day test can't have complete day-30 outcomes, and ask when the readout was produced before leaning on the retention figure.

  2. 2Hypothesis stated as factReframe it as a hypothesisTargeted repair

    What we’d changePresent it as a possible explanation to check against refund reasons, not a finding.

  3. 3Hypothesis stated as factReframe it as a hypothesisTargeted repair

    Source: RefundsRefund requests during the test: A 212, B 301 (+42%).

    What we’d changeThe refund rise is consistent with confusion; it doesn't confirm it. Check refund reasons before saying so.

  4. 4Numbers wrongRedo the arithmeticQuick edit

    Source: Readout summaryRevenue per trial start at day 30: A $9.42, B $9.61 (CI crosses zero).

    What we’d changeAn interval crossing zero means the revenue lift is uncertain, not illusory. Make the next step specific.

Claims the judge couldn’t find in the brief

  • Variant B’s presentation creates subscriber confusion or misleading expectations at checkout.

Grades and run details

Decision model 65 · LLM judge 5 of 11 checks
Decision model checks
  • partialUses the supplied evidence correctly22%
  • passAddresses the actual decision41%
  • passRespects explicit constraints86%
  • partialIdentifies material uncertainty51%
  • failAvoids unsupported claims59%
  • passProduces the required deliverable93%
  • passChecks guardrails before declaring a winner100%
  • partialSeparates effect from explanation47%
  • failTrusts the data before reading it99%
  • passGets the base of every number right89%
Run
Run
#1
Time to output
6 s
Submitted
25 Sept 2026

Usable with a quick edit9 of 10 checks passed

The recommendation and guardrail checks are sound and supported; only a data-trust check is missing and can be added in one sentence. No evidence or reasoning would need to be redone.

Output

Recommendation: Do not ship variant B to 100% on Monday. Keep A live while we investigate and revise B.

B did improve trial-to-paid conversion: 13.1% vs. 11.8%, a +1.3pp lift with a 95% CI of +0.6 to +2.0pp. But that gain comes with two problems against the guardrails agreed before launch:

  • Day-30 retention fell 4.5pp (95% CI −7.9 to −1.1), exceeding the maximum tolerated 2pp decline at the point estimate.
  • Refund requests rose 42%, well above the 10% limit. With an equal split, the raw counts also imply a higher request rate in B; we should confirm the rate and uncertainty before treating the size of the increase as settled.

The revenue case does not offset those risks. Day-30 revenue per trial start was only $0.19 higher in B, and its confidence interval crosses zero, so we don’t have clear evidence of a revenue gain.

That matters because B foregrounds the annual plan’s $9.99 monthly equivalent while hiding the monthly plan behind “See all plans.” The results are consistent with the concern that this framing may lift initial purchases while leaving more customers dissatisfied or less likely to remain. We should not trade clear retention and refund guardrail breaches for a conversion lift whose revenue benefit is unproven.

For Monday, I’d recommend holding A, reviewing refund reasons and plan mix, and testing a revised B that makes the $119.99 upfront annual charge and monthly option equally clear. Ship only if a follow-up test meets the pre-agreed retention and refund guardrails.

Grades and run details

Decision model 95 · LLM judge 10 of 11 checks
Decision model checks
  • passUses the supplied evidence correctly36%
  • passAddresses the actual decision99%
  • passRespects explicit constraints17%
  • passIdentifies material uncertainty78%
  • passAvoids unsupported claims34%
  • passProduces the required deliverable93%
  • passChecks guardrails before declaring a winner88%
  • passSeparates effect from explanation80%
  • partialTrusts the data before reading it17%
  • passGets the base of every number right52%
Run
Run
#1
API response time
10 s
Submitted
29 Sept 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 7

Uses the supplied evidence correctlyWrongWrongRight
Gemini 3.8 Flash · API

The output adds unsupported current-situation claims about user beliefs, proof of confusion, and future net-negative economics.

Gemini 3.5 Flash-Lite · Gemini

States the causal claim that Variant B's presentation 'creates subscriber confusion' as an established fact, which is not supported by the supplied context.

GPT-6 Luna · API

All stated facts and figures match the supplied readout, variant description, and guardrails, with no invented current-state claims.

Addresses the actual decisionMixedMixedRight
Gemini 3.8 Flash · API

It clearly recommends not shipping B, but does not state what result or condition would change that recommendation.

Gemini 3.5 Flash-Lite · Gemini

Does not state what result or condition would change the recommendation; it commits to 'do not ship' but does not specify what would alter that call.

GPT-6 Luna · API

It commits immediately to not shipping B to 100% and states the condition for revisiting: a follow-up test meeting the guardrails.

Respects explicit constraintsWrongRightRight
Gemini 3.8 Flash · API

It is under 400 words and leads with the recommendation, but it is not addressed to Priya Nair as requested.

Gemini 3.5 Flash-Lite · Gemini

Output is a recommendation to Priya Nair, well under 400 words, and respects all stated constraints.

GPT-6 Luna · API

It leads with the recommendation, stays under 400 words, and is framed for the named decision-maker.

Identifies material uncertaintyWrongWrongRight
Gemini 3.8 Flash · API

It does not name material unknowns that could change the decision or how they would be resolved.

Gemini 3.5 Flash-Lite · Gemini

Does not name specific unknowns that could change the decision or how they would be resolved.

GPT-6 Luna · API

It names refund-rate uncertainty and the revenue CI crossing zero, and proposes reviewing refund reasons and testing a revised variant to resolve them.

Avoids unsupported claimsWrongWrongRight
Gemini 3.8 Flash · API

It presents the misreading/bill-shock mechanism and future harm as established fact rather than hypotheses.

Gemini 3.5 Flash-Lite · Gemini

Presents the explanation of subscriber confusion as confirmed fact, not as a hypothesis.

GPT-6 Luna · API

The framing mechanism is presented as consistent with a concern, not asserted as established fact.

Produces the required deliverableMixedRightRight
Gemini 3.8 Flash · API

It is a usable recommendation in form, but it is not addressed to the named reader and contains major unsupported claims.

Gemini 3.5 Flash-Lite · Gemini

Provides a clear recommendation with reasoning for the named reader, within the word limit, and is usable as-is.

GPT-6 Luna · API

It is a complete, actionable recommendation with reasoning and next steps that Priya could use directly.

Separates effect from explanationWrongWrongRight
Gemini 3.8 Flash · API

It treats the conversion lift as an illusion driven by bill shock and user confusion as proven, not as a hypothesis.

Gemini 3.5 Flash-Lite · Gemini

States 'This confirms...creates subscriber confusion or misleading expectations' without labeling it as hypothesis, treating explanation as a finding.

GPT-6 Luna · API

Explanations are labelled as concerns or consistency, while the observed effects are reported as results.

All got wrong 1

Trusts the data before reading itWrongWrongWrong
Gemini 3.8 Flash · API

It does not check trust signals such as the 50/50 split, sample size, exposure, logging, or novelty before interpreting the result.

Gemini 3.5 Flash-Lite · Gemini

Does not check any trust signal in the data (e.g., sample ratio against 50/50 split, novelty effects) before interpreting results.

GPT-6 Luna · API

It does not explicitly check any data-trust signal such as the sample split against the intended 50/50 ratio before relying on the results.

All got right 2

Checks guardrails before declaring a winnerRightRightRight
Gemini 3.8 Flash · API

It explicitly checks both pre-agreed guardrails: retention and refunds.

Gemini 3.5 Flash-Lite · Gemini

Checks both guardrails (retention drop and refund increase) before making the call.

GPT-6 Luna · API

It explicitly evaluates both pre-agreed retention and refund guardrails.

Gets the base of every number rightRightRightRight
Gemini 3.8 Flash · API

The stated percentages, differences, and comparisons are computed from the supplied bases and are arithmetically sound.

Gemini 3.5 Flash-Lite · Gemini

All percentages and differences are correctly calculated from the supplied data, and ambiguous bases are not an issue.

GPT-6 Luna · API

All derived figures, including 1.3pp, −4.5pp, 42%, and $0.19, check out against the supplied data.

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Calibrated: the graders match our PM on 88% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT100.095.02None
2GPT-6.1 SolwithAPI94.790.52None
3GPT-6 LunawithAPI91.990.52None
4Sonnet 5.5withAPI81.775.92None
5Opus 5.5withClaude73.657.32None
6Gemini 3.5 Flash-LitewithGemini60.357.721 capped
7Gemini 3.8 FlashwithAPI55.624.121 capped

About the task

The PM job

Reading a test readout and deciding what to do next.

Why it matters

Experiment readouts are where false confidence is cheapest to produce and most expensive to act on. A model that declares a winner on conversion while retention quietly falls will ship the wrong thing.

What good looks like

  • Checks the result can be trusted before interpreting it
  • Reports effects with their uncertainty
  • Checks guardrail metrics before declaring a winner
  • Separates what the data shows from plausible explanations
  • Recommends a next step proportionate to the evidence

Deliberately not measured

  • Re-running the statistics from raw data
  • Chart production
Capability tested

Interpreting results within their limits

The failure we’re looking for

Claims causality, ignores guardrails or recommends arbitrary testing

Grading

Decision model and LLM judge, calibrated against a blind PM review