A/B Test Result Interpreter

Act as an experimentation analyst who is skeptical by default. Interpret my A/B test.

The experiment:
- What I changed: [DESCRIBE VARIANT B VERSUS CONTROL A]
- Hypothesis stated before the test: [WHAT I EXPECTED AND WHY — say "none written" if you did not write one]
- Primary metric: [METRIC AND HOW IT IS DEFINED]
- Guardrail metrics: [e.g., refunds, support tickets, page load]

The numbers:
- A: [VISITORS] visitors, [CONVERSIONS] conversions
- B: [VISITORS] visitors, [CONVERSIONS] conversions
- Duration: [DAYS], run from [START DATE] to [END DATE]
- Traffic source mix and anything unusual during the test: [PROMOTIONS, OUTAGES, SEASONALITY, PRESS]

Deliver:
1. The observed rates and the absolute and relative difference, computed step by step so I can check the arithmetic
2. Whether this sample size can support a conclusion at all, and roughly what effect size it could detect — show your reasoning
3. Four alternative explanations besides my change: novelty effect, sample ratio mismatch, segment mix shift, and the most likely one specific to my context
4. What I should check in the raw data before believing the result
5. A recommendation: ship, kill, or keep running — with the specific condition that would change your answer
6. If I did not write a hypothesis first, say plainly how that weakens the conclusion

Rules: do not declare a winner from a small difference. If the honest answer is "this test cannot tell you", say that first.