Lesson ProgressPhase 5 of 6
Phase 5Assessment
Assessment: Outliers and Data Quality
Demonstrate understanding through formative assessment and peer evaluation
Outlier Detection Exit Ticket
Demonstrate your ability to detect outliers, make data quality decisions, and explain your reasoning using business context.
Outlier Detection & Data Quality Assessment
Show that you can identify unusual values, interpret z-scores, and make appropriate data cleaning decisions
1. What does a z-score measure?
2. According to the ±2 standard deviation rule, which transaction is most likely an outlier?
3. Sarah finds a $0.10 transaction in the data. The z-score is only -1.2, so it's not a statistical outlier. What should she do?
4. Why should Sarah calculate statistics both with and without outliers?
5. When presenting analysis to the café manager, what is Sarah's BEST approach to outliers?
0 of 5 questions answered
What You've Mastered
🔍 Detection
- ✓ Calculate z-scores correctly
- ✓ Apply the ±2 rule
- ✓ Identify outliers in both directions
⚖️ Decision-Making
- ✓ Distinguish errors from real events
- ✓ Choose keep/flag/remove appropriately
- ✓ Consider business context
📊 Analysis
- ✓ Calculate with and without outliers
- ✓ Compare impact on statistics
- ✓ Document decisions
💼 Communication
- ✓ Explain reasoning to stakeholders
- ✓ Defend data quality choices
- ✓ Present cleaned vs. raw analysis