{"blocks": [{"key": "e96ecb57", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "b44786b5", "text": "Data scientist is interviewed on A/B-testing know-how for an online product.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "adfbee1d", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "811d4d01", "text": "Explain what a p-value represents; define Type I and Type II errors; outline the end-to-end experimentation workflow; describe Simpson's paradox and how to detect it; propose primary/secondary metrics; name two causal-inference methods useful when randomization is impossible and when you would apply them.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "c6c9b7b7", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "d560535f", "text": "Cover hypothesis, sample-size, segmentation, lift vs variance, DAGs or matching, and practical examples.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}