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When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
Best practices for creating comprehensive Jupyter notebook data analyses with statistical rigor, outlier handling, and publication-quality visualizations
Decision-first data analysis with statistical rigor gates. Use when analyzing CSV, JSON, database exports, API responses, logs, or any structured data to support a business decision. Handles: trend analysis, cohort comparison, A/B test evaluation, distribution profiling, anomaly detection. Do NOT use for codebase analysis (use codebase-analyzer), codebase exploration (use explore-pipeline), or ML model training.
Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.