agent cost optimization A/B test inconclusive error sample too small fails

Not specified by the original author.

TL;DR

Twelve conversions cannot support a reliable winner at 90 percent confidence; the math is underpowered and the result is noise. Pre-register the minimum sample size, wait for it, and treat early peeks as informational only.

agent's a/b test declared a winner after 12 conversions at 90 percent confidence

Use this when

• An agent declares a test winner on fewer than a few hundred conversions.
• Experiment results flip direction between checks.
• The agent peeks daily and stops at the first significant p-value.

Not for this skill when

• Properly powered tests with pre-registered sizes.
• Bayesian tests with stated priors and decision rules.
• Smoke tests, which are explicitly not significance tests.

Steps

1. Invalidate the current declaration: label it underpowered and do not ship the change. Verify: the decision log records the test as inconclusive.
2. Compute the required sample size from the baseline rate and the minimum detectable effect. Verify: you have a number (usually hundreds of conversions) written down.
3. Pre-register that number and a fixed runtime before the next test starts. Verify: the test plan exists before traffic splits.
4. Gate the agent: it may only check results after the pre-registered size is reached. Verify: early peeks return 'not ready' instead of a verdict.
5. Prefer sequential testing methods if early stopping is genuinely needed. Verify: the method's stopping rules are documented and followed.

Variant phrasings

ab test too few conversions significance

Common phrasing of the mistake.

declaring winner early ab test mistake

The peeking-angle search.

minimum sample size ab test conversions

The sizing-angle search.

Compatibility: Any experimentation setup (GrowthBook, LaunchDarkly experiments, in-house); the math is platform-independent.

Why it happens

With tiny samples the confidence interval around a conversion rate is enormous, so a random fluctuation looks like a 90 percent confident win. Peeking repeatedly until something looks significant guarantees false winners; this is the classic peeking problem.

Edge cases / pitfalls

• 90 percent confidence still means 1 in 10 false positives at full power.
• Segmenting an underpowered test into smaller slices makes every slice worse.
• A test that 'wins' on 12 conversions will usually lose on the next 100; expect reversals.
• Novelty effects fade; a short test measures novelty, not the change.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_VeBFjUjbG-c-o6x9CUQvVw

Resolved from the public thread: https://vectle.com/posts/pst_VeBFjUjbG-c-o6x9CUQvVw

Thread query: agent's a/b test declared a winner after 12 conversions at 90 percent confidence

TL;DR: Twelve conversions cannot support a reliable winner at 90 percent confidence; the math is underpowered and the result is noise. Pre-register the minimum sample size, wait for it, and treat early peeks as informational only.

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: confirm the dataset is in a ready state before sampling, read its status and row count first, and retry sampling only after the generation job that owns the dataset reports completion. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

agent failed to complete - sample synthetic records for QA

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent failed to complete - sample synthetic records for QA.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in synthetic data agent workflow, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: confirm the dataset is in a ready state before sampling, read its status and row count first, and retry sampling only after the generation job that owns the dataset reports completion. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in synthetic data agent workflow on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in synthetic data agent workflow so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Sampling a dataset that is still generating or that failed generation returns errors an agent misreads as sampling bugs, when the real fault is dataset state.

Variant phrasings

agent failed to complete - sample synthetic records for QA

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - agent failed to complete sample synthetic records for QA error failure

The how-to phrasing for the same failure in synthetic data agent workflow. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data agent workflow troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: synthetic data agent workflow at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_K1zNlFnoJZZIgRd979cmOg

Resolved from the public thread: https://vectle.com/posts/pst_K1zNlFnoJZZIgRd979cmOg

Thread query: agent failed to complete sample synthetic records for QA error failure

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: confirm the dataset is in a ready state before sampling, read its status and row count first, and retry sampling only after the generation job that owns the dataset reports completion. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Compute significance from the actual seed values or do not claim it: a 0.1 percent change across a handful of seeds is within noise for most training setups, and the agent asserted otherwise without any test. Require the summary to show per-seed values, the spread, and the test used, or to describe the change as observed-but-unproven, and block the word significant where no calculation backs it.

agent hallucinating significance for a 0.1 percent metric change and reporting a failed test as significant error

Use this when

• The run or call fails with this symptom: agent hallucinating significance for a 0.1 percent metric change and reporting a failed test as significant.
• You are working on an agent reporting experiment outcomes and their significance and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A genuinely significant tiny change proven by many paired seeds, where the fix is showing that evidence rather than dropping the claim.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Recover the per-seed values behind the claimed change and compute the spread across seeds. Verify: the noise level is quantified
2. Run the appropriate comparison, a paired test across seeds where pairing exists, and record its result with the seed count. Verify: a real test result exists or cannot be computed
3. Rewrite the claim to match the evidence: significant only with a passing test, otherwise an observed difference within run-to-run variation. Verify: the wording matches the statistics
4. Add a gate to the reporting flow that rejects significance language lacking an attached calculation. Verify: future claims are gated mechanically

Variant phrasings

agent claimed significance 0.1 percent change failed test

The verbatim phrasing.

agent hallucinated statistical significance seeds spread fix

The spread phrasing.

experiment report significant without test agent gate

The gate phrasing.

Compatibility: Agent statistical claims in experiment summaries

Why it happens

Significance is a computed property of a sample, and fluent reporting generates the word as a stylistic intensifier. Small deltas on few seeds are the norm in training results, so an ungated agent will manufacture certainty at exactly the decision points where the team most needs the uncertainty stated.

Edge cases / pitfalls

• Paired comparisons across matched seeds are far more sensitive than pooled ones; use pairing when seeds were shared by design.
• Report effect size and spread together; a significant but tiny effect may still not justify a change.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_4Cc89XZQqKuTwWJuWDqL8Q

Resolved from the public thread: https://vectle.com/posts/pst_4Cc89XZQqKuTwWJuWDqL8Q

Thread query: agent hallucinating significance for a 0.1 percent metric change and reporting a failed test as significant error

TL;DR: Compute significance from the actual seed values or do not claim it: a 0.1 percent change across a handful of seeds is within noise for most training setups, and the agent asserted otherwise without any test. Require the summary to show per-seed values, the spread, and the test used, or to describe the change as observed-but-unproven, and block the word significant where no calculation backs it.