01 · Task
Objective and constraints
Test how three model outputs handle a conversion comparison that cannot isolate causality.
- Keep the task packet and generation parameters fixed while the model varies.
- Separate the observed rates from a causal claim.
- Require a next experiment that can reduce the stated uncertainty.
02 · Reproduction input
Complete prompt
Version A converted 8 of 100 visitors. Version B converted 14 of 100 visitors during the following seven days. No randomized split was used, and traffic sources differed between the weeks. Does this prove B is better? Give a decision recommendation and identify the uncertainty.
03 · Evaluation scope
Criteria used for this task
Strong answers say the observation does not prove causality, identify small samples and time/traffic confounding, and recommend a randomized concurrent test before a durable rollout.
- Separate observed rates from causality.
- Name non-random allocation and changing traffic sources.
- Recommend a concurrent randomized comparison.
04 · Immutable snapshots
Captured outputs and review
Start with the comparison summary. Open a snapshot only when you need the full output, run metadata, and editorial review.
OpenAI
openai/gpt-oss-20b
- Strength
- Names both statistical uncertainty and confounding
- Review boundary
- The possible confounders remain unverified; collect evidence before attributing the difference to any of them
Qwen
qwen/qwen3-30b-a3b-fp8
- Strength
- Directly rejects proof
- Review boundary
- Does not mention the small sample explicitly
Mistral AI
mistralai/mistral-small-3.1-24b-v2
- Strength
- Rejects proof and names both stated design flaws
- Review boundary
- Stops short of recommending a concurrent randomized test
Output 1Raw captured output · collapsed by defaultOpenAI · openai/gpt-oss-20b
Raw output
No – the data do not prove that Version B is superior.
Why not?
- The two weeks were not randomized; traffic sources (e.g., referral, paid, organic) differed, so any conversion difference could be due to those sources rather than the version.
- The sample size is small (100 visitors per week), so the observed 8 % vs. 14 % difference could be due to random variation.
- External factors (seasonality, day‑of‑week, marketing campaigns) could have changed between the weeks.
Decision recommendation
Run a properly randomized A/B test (or multivariate test) with a larger, balanced sample and control for traffic source. Only then can you statistically assess whether B truly outperforms A.
Uncertainty
- Confounding by traffic source and other time‑varying factors.
- Statistical uncertainty due to the small sample size.
- Potential interaction with other variables (device, location, etc.) that were not controlled.
meets
Strengths: Names both statistical uncertainty and confounding Recommends a balanced randomized test Labels additional possible confounders as hypotheses rather than observed events
Risks: The possible confounders remain unverified; collect evidence before attributing the difference to any of them
Best use: A decision memo that must explain uncertainty.
Output 2Raw captured output · collapsed by defaultQwen · qwen/qwen3-30b-a3b-fp8
Raw output
No, this does not prove B is better. The lack of a randomized split and differing traffic sources introduce confounding variables. Recommendation: Conduct a controlled experiment with random assignment. Uncertainty: Results may be influenced by external factors.
meets
Strengths: Directly rejects proof Identifies confounding and random assignment
Risks: Does not mention the small sample explicitly
Best use: A concise executive answer.
Output 3Raw captured output · collapsed by defaultMistral AI · mistralai/mistral-small-3.1-24b-v2
Raw output
No, this does not prove that Version B is better. The decision recommendation is to not conclude that Version B is better based on this data alone.
Uncertainty:
- No randomized split was used, introducing potential bias.
- Traffic sources differed between the weeks, which could affect conversion rates.
- The sample size is small, making the results less reliable.
partially meets
Strengths: Rejects proof and names both stated design flaws
Risks: Stops short of recommending a concurrent randomized test “Small” is asserted without quantifying uncertainty
Best use: A cautious conclusion needing a concrete next experiment.
05 · Claim ledger
Claims, sources, evidence, and boundaries
Claim 1
The observed conversion rate is higher in the second sample.
- Source
- Published task packet
- Support
- The packet reports an 8% rate for one sequential period and 14% for another.
- Boundary
- A difference in observed rates does not identify what caused the difference.
Claim 2
The packet does not prove that the page version caused the increase.
- Source
- Task design and captured-output review
- Support
- The samples occurred in different weeks with different traffic and without concurrent random assignment.
- Boundary
- The result remains a useful signal for a better controlled follow-up test.
06 · Review conclusion
What this evidence supports
A larger observed rate is evidence of a difference in these samples—not proof that the version caused it.
Known limitations
- No confidence interval or visitor-level data is supplied.
- The two samples are sequential rather than concurrent and randomized.
- Changing the model is the only controlled output variable in this Richbay comparison.
Reusable takeaway
Use the causality-boundary checklist and specify a concurrent randomized follow-up.
Use the supporting method