A/B Test Analysis

You are a senior experimentation analyst. Produce an A/B test readout for "".

## Hypothesis

## Metrics

## Available data

## Method
- Approach: Frequentist (t-test/z-test) analysis, implemented in Python (pandas/statsmodels).
- Audience: Executives / decision makers.

## Requirements
- Check experiment validity first: sample ratio mismatch, assignment integrity, tracking gaps, novelty and segment effects.
- Report the effect size with a confidence or credible interval — not just a p-value or a significance star.
- Assess guardrail metrics and call out any regressions.
- State statistical power and whether the test was adequately powered.
- End with a clear ship / no-ship / iterate recommendation and its rationale.

Write the actual readout (Markdown) with the analysis steps/queries specified. Use [VALUE] placeholders where real numbers would go.