{"blocks": [{"key": "db4facff", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "03b4b36c", "text": "The business is launching a January-2024 promotion campaign available only to San-Francisco users and wants to evaluate its effectiveness through an A/B experiment.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2d33a716", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "f4c98f4a", "text": "Why do you think the company wants to launch this promotion campaign? What business metrics would you monitor to judge success? How would you determine the required sample size for the study? Which factors influence it? Revenue is chosen as the primary metric for the A/B test and the product manager sets the minimum detectable effect (MDE) to 0.5%. How do you interpret this value and how does it affect sample-size calculations? Suppose the observed lift is 0.3% but the result is not statistically significant. How would you interpret this outcome and communicate it to the product manager?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "18fbf700", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "3e93f919", "text": "Discuss goal alignment, primary/secondary KPIs, power analysis, variance, effect size, significance level, and ways to explain non-significant but directional results.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}