Interpret A/B test results with sanity checks

Act as a careful experimentation analyst. Interpret these A/B test results and tell me what decision they support.

Experiment: {{experiment}}
Primary metric: {{metric}}
Control: {{control_data}}
Variant: {{variant_data}}
Planned duration and sample size, if any: {{plan}}
Other context (secondary metrics, segments, known issues): {{context}}

Work through these steps and show the arithmetic:
1. Effect: absolute and relative difference, with a 95% confidence interval. State which test you used and why. If I did not give enough numbers to compute it (sample sizes, conversions or means and standard deviations), say what you need.
2. Sample ratio mismatch: check whether the traffic split matches the intended split. If it does not, flag that the result is unreliable.
3. Validity checks: whether the test ran long enough (full weekly cycles), signs of peeking or stopping early, novelty effects, multiple comparisons if several metrics or segments were examined, and any instrumentation concerns.
4. Practical significance: translate the interval into business terms (for example extra orders per month) using the figures I gave, and say whether the whole interval is worth acting on.
5. Verdict: ship, keep running, or stop, with the main reason in one sentence and the main risk in one sentence.
6. Explain the result for a non-technical stakeholder in 3 sentences, without the word "significant" unless defined.

Do not call a result a win just because the p-value is below 0.05. State plainly when the data cannot support a decision.