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 18 graded outputs by 9 models. 56% 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 correctly97% 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. Catches the sample ratio mismatch89% pass
    The output computes the split (53.6% treatment), calls it a sample ratio mismatch, and states the cause must be found before trusting the result.
    Opus 5.5 · Claude · Up 6%, and significant

Where it slips

  1. Uses the supplied evidence correctly50% 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. Avoids unsupported claims56% 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
  3. Trusts the data before reading it60% pass
    Does not check any trust signal (e.g., sample ratio, exposure) before interpreting the results.
    GPT-6 Luna · 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 · v1.11

The outputs

9 setups answered this brief.

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 correctly94%
  • passAddresses the actual decision67%
  • passRespects explicit constraints97%
  • passIdentifies material uncertainty98%
  • passAvoids unsupported claims56%
  • passProduces the required deliverable72%
  • passInterprets power correctly88%
  • failTrusts the data before reading it23%
  • passGets the base of every number right76%
  • passDoesn't overread an unclear result95%
Run
Run
#1
API response time
9 s
Submitted
29 Sept 2026

Usable with a quick edit10 of 10 checks passed

The note is clear, evidence-based, and actionable; a PM could use it with only minor wording tweaks, without rechecking facts or redoing 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

  1. 1Numbers wrongRedo the arithmeticQuick edit

    Source: ReadoutActivation: 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.

Grades and run details

Decision model 100 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly66%
  • passAddresses the actual decision38%
  • passRespects explicit constraints51%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims70%
  • passProduces the required deliverable69%
  • passInterprets power correctly100%
  • passTrusts the data before reading it58%
  • passGets the base of every number right67%
  • passDoesn't overread an unclear result95%
Run
Run
#1
Time to output
21 s
Submitted
25 Sept 2026

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 90 · LLM judge 73
Decision model checks
  • passUses the supplied evidence correctly33%
  • passAddresses the actual decision20%
  • passRespects explicit constraints53%
  • passIdentifies material uncertainty98%
  • passAvoids unsupported claims46%
  • passProduces the required deliverable79%
  • passInterprets power correctly100%
  • failTrusts the data before reading it52%
  • passGets the base of every number right56%
  • passDoesn't overread an unclear result72%
Run
Run
#1
API response time
7 s
Submitted
8 Oct 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 3

Uses the supplied evidence correctlyRightRightMixed
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

All factual statements about the current situation are taken directly from the supplied context, with no invented numbers or facts.

Haiku 5.5 · API

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

Avoids unsupported claimsRightRightMixed
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

Interpretations like 'encouraging supporting evidence' are clearly judgments, and the conditional about ticket attribution does not assert a missing fact as established.

Haiku 5.5 · API

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

Trusts the data before reading itWrongRightWrong
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

It explicitly checks trust signals by recommending confirmation of the full activation window, experiment allocation, tracking, and comparison of confusion-ticket rates between arms.

Haiku 5.5 · API

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

All got right 7

Addresses the actual decisionRightRightRight
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

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.

Haiku 5.5 · API

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

Respects explicit constraintsRightRightRight
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

The note is addressed to the onboarding team, stays under 300 words, and proposes actions that would enforce the stated constraints.

Haiku 5.5 · API

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

Identifies material uncertaintyRightRightRight
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

It names the specific unknowns (possible activation loss, downstream harm, arm-level ticket rates) and says how they would be resolved or what would change the decision.

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.

Produces the required deliverableRightRightRight
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

The output is a complete, actionable note to the onboarding team, within the word limit, and a PM could act on it with light edits.

Haiku 5.5 · API

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

Interprets power correctlyRightRightRight
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

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.

Haiku 5.5 · API

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

Gets the base of every number rightRightRightRight
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

All percentages and differences are correctly stated with the right base (activation rates, absolute ticket counts), and no ambiguous base is left unclear.

Haiku 5.5 · API

Percentages and differences match the readout; no misstated denominators.

Doesn't overread an unclear resultRightRightRight
GPT-6 Luna · API

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

GPT-6 Astra · ChatGPT

The analysis states the result is inconclusive, does not claim a win or no effect, and correctly notes that the test cannot rule out a meaningful decline or a small benefit.

Haiku 5.5 · API

Calls the result inconclusive, not evidence of no effect, and states what the CI can and cannot 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 60% of 40 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT91.7100.02None
2Sonnet 5.5withAPI84.695.52None
3Opus 5.5withClaude88.386.42None
4GPT-6.1 SolwithAPI76.380.82None
5Haiku 5.5withAPI82.571.02None
6GPT-6 LunawithAPI78.364.72None
7Gemini 4 ArgonwithAPI68.865.72None
8Gemini 3.5 Flash-LitewithGemini53.351.72None
9Gemini 3.8 FlashwithAPI63.345.82None

What you get for the cost

Sonnet 5.5 · API scores within 5 points of GPT-6 Astra · ChatGPT at a third of the cost.

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 96, 107× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · API: task score 91, 35× the cheapestOpus 5.5 · Claude: task score 88, 95× the cheapest (1 of 2 outputs estimated)GPT-6.1 Sol · API: task score 78, 14× the cheapestHaiku 5.5 · API: task score 76, 1.8× the cheapestGPT-6 Luna · API: task score 70, the cheapestGemini 4 Argon · API: task score 64, 14× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 53, 3.1× the cheapest (1 of 2 outputs estimated)Gemini 3.8 Flash · API: task score 52, 12× the cheapestGPT-6 Astra · ChatGPTSonnet 5.5 · APIOpus 5.5 · ClaudeGPT-6.1 Sol · APIHaiku 5.5 · APIGPT-6 Luna · APIGemini 4 Argon · APIGemini 3.5 Flash-Lite · GeminiGemini 3.8 Flash · API
Best valueNobody 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 · API701.0×
Haiku 5.5 · API761.8×
Gemini 3.5 Flash-Lite · Gemini533.1× (partly estimated)
Gemini 3.8 Flash · API5212×
GPT-6.1 Sol · API7814×
Gemini 4 Argon · API6414×
Sonnet 5.5 · API9135×
Opus 5.5 · Claude8895× (partly estimated)
GPT-6 Astra · ChatGPT96107× (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