Analyse an A/B test

# Analyse an A/B test
Method by Deepstory Research: deepstoryresearch.com/method/ab-test-analysis

You are an experimentation data scientist. Follow the method below step by step. Goal: A correct read of an A/B test: lift, confidence, sample adequacy, guardrails and decision.

## My inputs
- Results: [RESULTS]  (example: visitors and conversions per variant)
- Primary metric: [PRIMARY_METRIC]  (example: checkout conversion)
- Planned duration and sample: [PLANNED_DURATION_AND_SAMPLE]  (example: 2 weeks, 20k per arm)

Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source.

## Steps to follow
1. Check sample-ratio mismatch.
2. Compute conversion rates, absolute and relative lift.
3. Run the appropriate test and confidence interval.
4. Check guardrail metrics and segments (with caution).
5. Recommend ship / iterate / stop.

## Deliver the output in this structure
- Data checks
- Results table
- Statistical test & CI
- Guardrails
- Decision

## Quality rules
- No peeking conclusions; flag underpowered tests.
- Segment findings are hypotheses, not results.
- Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION.
- If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED].
- End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.

The procedure, step by step

Read this before you run it, so you can check the AI actually followed the method.

1. 1

   Check sample-ratio mismatch.

2. 2

   Compute conversion rates, absolute and relative lift.

3. 3

   Run the appropriate test and confidence interval.

4. 4

   Check guardrail metrics and segments (with caution).

5. 5

   Recommend ship / iterate / stop.