You ran an A/B/n experiment with 1 control and 2 treatment variants. The primary metric is **conversion rate** (each user either converts or not within the experiment window). Users are independently assigned to groups. You are given the following aggregated results: | group | users (n) | conversions (x) | |---|---:|---:| | control | 50,000 | 5,000 | | variant_a | 50,000 | 5,250 | | variant_b | 50,000 | 5,400 | Assumptions: - Two-sided tests unless specified. - Significance level b1 = 0.05. - Metric is a binomial proportion; large-sample normal approximations are acceptable. Tasks: 1) In **Python**, compute the **p-value** for each variant vs control using an appropriate statistical test for proportions. 2) Because there are two variants, address **multiple comparisons** (e.g., Bonferroni or FDR). Report adjusted significance conclusions. 3) Provide 95% confidence intervals for the uplift (absolute and/or relative) for each variant vs control. 4) Make a **ship / no-ship** recommendation. Explain what additional checks you would do before shipping (e.g., guardrail metrics, segmentation concerns, SRM, novelty effects), and how you would communicate uncertainty to stakeholders. (You may use `statsmodels`/`scipy` or implement the formulas directly.)