{"blocks": [{"key": "92aed1b7", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "e5756332", "text": "Analyzing distribution of the number of comments each article receives on a content website.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "255768d7", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "6f37ea1f", "text": "You receive the full distribution of comment counts per article. Which summary statistics would you compute and why? When might the mean be misleading compared with the median? How would you detect and treat outliers in the comment counts? If product managers want to know whether a recent UI change increased engagement, which statistical test would you choose and why?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "d53e7b78", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "3e2bbe8e", "text": "Think about skewed count data, heavy-tailed distributions, robust statistics, and basic hypothesis testing.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}