{"blocks": [{"key": "d3df1ceb", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "0f10e2f4", "text": "You receive raw log-level data from an A/B test and must convince stakeholders of the result.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "53e9bf74", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2da34686", "text": "Clean the data in Python, compute primary metrics (e.g., conversion, lift, p-value), produce at least one visualization that supports your conclusion, and clearly interpret the outcome for the business.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "9593a3b5", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "b1cf5db5", "text": "Use pandas for ETL, seaborn/matplotlib for plots, and a two-sample t-test or proportion z-test for significance.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}