Tasks / Metrics & Experimentation

Analyse experiment results

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

Task type v1.2 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 16 graded outputs by 8 models. 63% were usable with at most a quick edit.

Reliably right

  1. Respects explicit constraints100% pass
    The note is addressed to the onboarding team, stays under 300 words, and proposes actions that would enforce the stated constraints.
    GPT-6 Astra · ChatGPT · The underpowered onboarding test
  2. Interprets power correctly100% pass
    It correctly explains that the test was designed to detect a 3pp effect, that the CI includes both harm and benefit, and that extending would detect about 2pp.
    GPT-6 Astra · ChatGPT · The underpowered onboarding test
  3. Addresses the actual decision92% pass
    The output commits to a staged rollout as the recommended action, and specifies conditions (commercial unacceptability of a 1.4pp loss or value of a 2pp improvement) that would change the call to extending the test.
    GPT-6 Astra · ChatGPT · The underpowered onboarding test

Where it slips

  1. Uses the supplied evidence correctly56% pass
    States that the change is easy to reverse, which is not supported by the supplied context.
    Haiku 5.5 · API · The underpowered onboarding test
  2. Gets the base of every number right59% pass
    The Week 1 lift figure of +4.0% is a miscalculation; the correct rate from the supplied data is about 5.6%.
    Haiku 5.5 · API · Up 6%, and significant
  3. Avoids unsupported claims59% pass
    Presents 'the change is easy to reverse' as fact without evidence from the brief or context.
    Haiku 5.5 · API · The underpowered onboarding test

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Trusts the data before reading it

    Before interpreting effects, does the analysis check whether the result is trustworthy (sample ratio, exposure or logging issues, novelty, a surprisingly large lift) and say what it found?

    Passes when Explicitly checks at least one trust signal in the supplied data, such as the split against the intended ratio, and treats a surprising result as a reason to investigate before acting.

  2. Doesn't overread an unclear result

    Does the analysis state honestly what the data can and can't show: no win claimed from an early peek or a result that isn't significant, and no 'no effect' concluded from a test too small to detect one?

    Passes when Every claim matches the strength of the evidence, and anything unclear is called unclear, with what it can't rule out.

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Gets the base of every number right · and 1 written for each task, which you’ll see in the tasks below.

The tasks

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

The brief

We tested a shorter onboarding checklist and the result came back 'not significant'. The team wants to call it a failure and move on. Write a short note to the onboarding team saying what we should conclude from this test and what we should do next. Keep it under 300 words.

What the model was given4 items: Scenario, Readout, Qualitative, Options on the table
ScenarioTrellis is a project-management tool for small teams. The shorter checklist cuts onboarding from nine steps to five. It is already built: shipping it to everyone would take about a day of engineering. Activation means creating a first project and inviting at least one teammate within seven days.
ReadoutActivation: A 31.2%, B 32.9% (+1.7pp, 95% CI −1.4 to +4.8). n = 3,960 per arm. The pre-registered minimum detectable effect was 3pp.
QualitativeSupport tickets tagged 'onboarding confusion' fell from 44 to 29 during the test.
Options on the tableRunning the test for another four weeks at current traffic would detect an effect of about 2pp.
What a strong answer doesThe answer key the graders mark against

Inconclusive, not a failure: the test could not detect effects smaller than ~3pp, and the point estimate is positive. Recommend a decision based on cost of shipping versus a longer test.

Critical failures (cap the score at 40)
  • Concludes the change has no effect
Task

Mid-level · v1.10

The outputs

8 setups answered this brief.

Usable with a quick edit7 of 10 checks passed

The core reasoning is sound; the unsupported claim about reversibility can be edited out or qualified without redoing the analysis.

Output

To: Onboarding team Re: Shorter checklist test

Conclusion: This wasn't a failure. It was inconclusive, and the result doesn't support "no effect."

  • The point estimate was +1.7pp (32.9% vs. 31.2%).
  • The 95% CI runs from −1.4pp to +4.8pp. Effects from slightly negative to clearly useful are all consistent with the data.
  • The test was powered for a 3pp lift. A real 1–2pp improvement, which would still be worth having, would usually not reach significance at this sample size.
  • Support tickets for "onboarding confusion" fell from 44 to 29. This wasn't a pre-registered metric, and counts this small are noisy, but it points the same way.

Recommendation: Don't treat this as a loss, and don't run the test indefinitely.

  1. Ship the five-step checklist to everyone, with a 10% holdout on the old flow. Shipping costs about one day of engineering, the change is easy to reverse, and the evidence leans positive. The holdout keeps measuring the real effect at no extra build cost.
  2. Decide the extension question on decision value, not on the test result. Another four weeks would detect roughly 2pp. That is worth doing only if a 2pp lift would change what we do next. If it wouldn't, the holdout gives us enough information.
  3. If we extend, make it a new pre-registered analysis. Set the new target effect and sample size now, and don't keep peeking at the current data until it crosses significance. Changing the goalposts after an inconclusive result is how teams end up with false positives.

Bottom line: The checklist is probably neutral to helpful, and it is cheap to keep. Ship it with a holdout, track activation and tickets, and revisit in four weeks.

Claims the judge couldn’t find in the brief

  • The change is easy to reverse

Grades and run details

Decision model 85 · LLM judge 73
Decision model checks
  • passUses the supplied evidence correctly22%
  • passAddresses the actual decision69%
  • passRespects explicit constraints53%
  • passIdentifies material uncertainty100%
  • partialAvoids unsupported claims59%
  • passProduces the required deliverable88%
  • passInterprets power correctly100%
  • failTrusts the data before reading it52%
  • passGets the base of every number right73%
  • passDoesn't overread an unclear result64%
Run
Run
#1
API response time
7 s
Submitted
8 Oct 2026

Usable with a quick edit9 of 10 checks passed

The output is clear and actionable; a PM could use it with minor edits, and the missing trust check is a small omission that does not undermine the core reasoning.

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.

Grades and run details

Decision model 90 · LLM judge 91
Decision model checks
  • passUses the supplied evidence correctly82%
  • passAddresses the actual decision96%
  • passRespects explicit constraints96%
  • passIdentifies material uncertainty97%
  • passAvoids unsupported claims49%
  • passProduces the required deliverable62%
  • passInterprets power correctly89%
  • failTrusts the data before reading it27%
  • passGets the base of every number right95%
  • passDoesn't overread an unclear result91%
Run
Run
#1
API response time
9 s
Submitted
29 Sept 2026

Usable with a quick edit7 of 10 checks passed

The unsupported claim about ticket splitting is minor and can be fixed with a quick edit; the core reasoning and recommendation remain sound and usable.

Output

Shorter checklist test — what it tells us and what's next

Hi all,

The shorter onboarding checklist came back "not significant." That doesn't make it a failure, and I don't think we should drop it.

What the test actually says

  • B activated at 32.9% vs 31.2% for A, a +1.7pp lift.
  • The 95% interval runs from −1.4pp to +4.8pp. Most of that range is positive, and the worst plausible case is a small loss.
  • We sized the test to detect 3pp. A real 1–2pp gain was always likely to come back "not significant." The result means the effect is smaller than 3pp or zero.1 It doesn't mean the change didn't work.

Supporting signal

Support tickets tagged "onboarding confusion" fell from 44 to 29 during the test. We didn't split tickets by arm, so this is directional only. It does point the same way as the activation result.

Recommendation: ship it

  • It's already built, and rolling it out costs about a day.
  • The likely effect is neutral to positive, and five steps are simpler to maintain than nine.
  • Running four more weeks would only detect effects of about 2pp. Our point estimate is 1.7pp, so we would probably still get an ambiguous answer, a month later.

Guardrails

  • Watch 7-day activation for the four weeks after rollout. If it drops more than about 1.5pp below the current baseline, roll back.
  • Split onboarding-confusion tickets by checklist version from now on, so next time we can tell whether a ticket drop is real.

For future tests

Before launch, agree what we'll do if the result comes back inconclusive. For a cheap, low-risk change like this, "not significant but probably not harmful" should default to ship.

Happy to discuss.

What a PM had to fix

  1. 1Numbers wrongRedo the arithmeticTargeted repair

    Source: ReadoutActivation: A 31.2%, B 32.9% (+1.7pp, 95% CI −1.4 to +4.8).

    What we’d changeCorrect the reading: the interval runs from −1.4 to +4.8pp, so an effect above 3pp isn't ruled out. The test just wasn't sized to reliably detect smaller ones.

Claims the judge couldn’t find in the brief

  • We didn't split tickets by arm.

Grades and run details

Decision model 70 · LLM judge 73
Decision model checks
  • failUses the supplied evidence correctly40%
  • passAddresses the actual decision92%
  • passRespects explicit constraints49%
  • passIdentifies material uncertainty97%
  • partialAvoids unsupported claims20%
  • passProduces the required deliverable87%
  • passInterprets power correctly99%
  • failTrusts the data before reading it72%
  • passGets the base of every number right80%
  • partialDoesn't overread an unclear result34%
Run
Run
#1
Time to output
13 s
Submitted
25 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 2

Uses the supplied evidence correctlyMixedRightWrong
Haiku 5.5 · API

States that the change is easy to reverse, which is not supported by the supplied context.

GPT-6 Luna · API

All factual statements about the current situation are taken directly from the supplied context.

Opus 5.5 · Claude

Claims that tickets were not split by arm is not stated in the context and is presented as fact.

Avoids unsupported claimsWrongRightWrong
Haiku 5.5 · API

Presents 'the change is easy to reverse' as fact without evidence from the brief or context.

GPT-6 Luna · API

Interpretations are clearly labelled as such, and no unsupported claims are presented as fact.

Opus 5.5 · Claude

Presents 'We didn't split tickets by arm' as an established fact when the context only gives total ticket counts.

All got wrong 1

Trusts the data before reading itWrongWrongWrong
Haiku 5.5 · API

Does not explicitly check any trust signal (e.g. split ratio, exposure) before interpreting the data.

GPT-6 Luna · API

Does not check any trust signal (e.g., sample ratio, exposure) before interpreting the results.

Opus 5.5 · Claude

Does not check any trust signal (e.g., sample ratio, exposure) before interpreting the result.

All got right 7

Addresses the actual decisionRightRightRight
Haiku 5.5 · API

Commits early to 'ship the five-step checklist' with a holdout, framed for the onboarding team.

GPT-6 Luna · API

Commits to extending the test and specifies that the decision after extension will use both activation and confusion data.

Opus 5.5 · Claude

Commits to 'ship it' early, and specifies a guardrail (activation drop >1.5pp) that would trigger rollback.

Respects explicit constraintsRightRightRight
Haiku 5.5 · API

Under 300 words (approx. 266), addressed to the onboarding team, note format.

GPT-6 Luna · API

The note is under 300 words, addressed to the team, and respects the requested form.

Opus 5.5 · Claude

Output is a short note to the onboarding team, under 300 words, addressing conclusion and next steps.

Identifies material uncertaintyRightRightRight
Haiku 5.5 · API

Names the CI and effect-size uncertainty, and says extending is worth doing only if a 2pp lift would change what they do.

GPT-6 Luna · API

Identifies the imprecision of the test and proposes a longer test to resolve the uncertainty.

Opus 5.5 · Claude

Names the undetectable small effect, the directional-only ticket data, and the CI range; says what would change the call.

Produces the required deliverableRightRightRight
Haiku 5.5 · API

Short note with conclusion and clear next steps; a PM could act on it with light edits.

GPT-6 Luna · API

The note is complete, under 300 words, and the team could act on it without filling major gaps.

Opus 5.5 · Claude

A complete, actionable note to the onboarding team within the word limit.

Interprets power correctlyRightRightRight
Haiku 5.5 · API

Explains the test could detect 3pp, and that a real 1-2pp improvement would not reach significance.

GPT-6 Luna · API

Explains that the test was not precise enough and that an extension would detect effects around 2pp.

Opus 5.5 · Claude

Explains the test could only detect ≥3pp, so a 1-2pp real gain would likely be non-significant.

Gets the base of every number rightRightRightRight
Haiku 5.5 · API

Percentages and differences match the readout; no misstated denominators.

GPT-6 Luna · API

All derived figures (1.7pp, CI, ticket drop) are correctly stated and no ambiguous bases are used.

Opus 5.5 · Claude

Percentages and differences are correctly stated; ticket numbers are absolute, not misrepresented as rates.

Doesn't overread an unclear resultRightRightRight
Haiku 5.5 · API

Calls the result inconclusive, not evidence of no effect, and states what the CI can and cannot rule out.

GPT-6 Luna · API

Correctly calls the result inconclusive and does not claim a win or no effect from an underpowered test.

Opus 5.5 · Claude

Does not claim a win or conclude no effect; correctly calls the result inconclusive and states what it can't rule out.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 63% of 40 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT95.8100.02None
2Sonnet 5.5withAPI88.395.52None
3Opus 5.5withClaude80.886.42None
4GPT-6.1 SolwithAPI89.280.82None
5Haiku 5.5withAPI88.371.02None
6GPT-6 LunawithAPI88.864.72None
7Gemini 3.8 FlashwithAPI69.645.82None
8Gemini 3.5 Flash-LitewithGemini54.651.72None

What you get for the cost

No setup scores within 5 points of the leader at half its cost or less here, so the leader is also the best value.

Task score against relative cost for each setup. The table below has the same figures.4050607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 98, 107× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · API: task score 92, 35× the cheapestOpus 5.5 · Claude: task score 84, 95× the cheapest (1 of 2 outputs estimated)GPT-6.1 Sol · API: task score 83, 14× the cheapestHaiku 5.5 · API: task score 78, 1.8× the cheapestGPT-6 Luna · API: task score 75, the cheapestGemini 3.8 Flash · API: task score 55, 12× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 53, 3.1× the cheapest (1 of 2 outputs estimated)GPT-6 Astra · ChatGPTSonnet 5.5 · APIOpus 5.5 · ClaudeGPT-6.1 Sol · APIHaiku 5.5 · APIGPT-6 Luna · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Nobody beats these on both score and costCost partly estimated (app runs with no usage meter)
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API751.0×
Haiku 5.5 · API781.8×
Gemini 3.5 Flash-Lite · Gemini533.1× (partly estimated)
Gemini 3.8 Flash · API5512×
GPT-6.1 Sol · API8314×
Sonnet 5.5 · API9235×
Opus 5.5 · Claude8495× (partly estimated)
GPT-6 Astra · ChatGPT98107× (partly estimated)

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

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