{"blocks": [{"key": "2c5ed5d5", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "76b4ea39", "text": "E-commerce platform tests two treatments (T1, T2) that affect Gross Bookings (GB) and Variable Consideration (VC)", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "ec267802", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "204d3f06", "text": "T1 shows no significant change in GB or VC, while T2 shows a significant GB increase but significant VC decrease. Explain these results to the PM and recommend next steps. Given T2 confidence intervals (GB [+0.1%, +2.3%] ≈ +$0.48/order; VC [–2.5%, –1.5%] ≈ –$0.20/order), decide whether to launch and justify. Design a segmentation analysis to identify cohorts where GB lifts without hurting VC. If we will run 20 parallel feature experiments, define launch criteria, statistical thresholds, and how you will control error rates.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "6faa7626", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "d6414335", "text": "Contrast statistical vs practical significance, revenue vs margin trade-offs, multiple-testing corrections, and cohort discovery techniques.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}