ENT 402 · Unit 2 of 6
Experiment Design and Learning Loops
Product-Market Fit and Startup Experimentation
Start unit · 4 lessons →Learning objectives
- Design experiments with clear success and kill metrics
- Apply "Experiment Design and Learning Loops" to a real venture decision
- Contribute to your Cohort analysis dashboard deliverable
Unit overview
| # | Lesson | Core idea |
|---|---|---|
| 1 | Understanding Experiment Design and Learning Loops | Core frameworks for this unit |
| 2 | How Experiment Design and Learning Loops Works in Practice | Core frameworks for this unit |
| 3 | Evaluating Trade-offs in Experiment Design and Learning Loops | Core frameworks for this unit |
| 4 | Experiment Design and Learning Loops: Case Analysis and Recommendations | Core frameworks for this unit |
Complete all four lessons, then finish unit assessments on this page.
Unit assessment
Complete each section below. Score 80%+ on the quiz to finish this unit's assessment.
Exercises
Apply what you learned in this unit with structured practice.
Deliverable
300–500 word analysis document saved to your portfolio under ENT 402.
Rubric
- • Framework applied correctly (not just named)
- • Specific evidence from a real example
- • Clear recommendation with tradeoffs acknowledged
- • Professional writing with source citation
Deliverable
Problem solutions + 150-word reflection in your ENT 402 workbook.
Rubric
- • Attempted all practice items before checking answers
- • Honest reflection on errors
- • Identifies a specific review action
Case analysis
Analyze a case using frameworks from this unit.
Deliverable
2-page case write-up in your portfolio.
Rubric
- • Case facts are accurate and sourced
- • Analysis uses unit frameworks explicitly
- • Recommendation is justified with tradeoffs
- • Risks are specific, not generic
Knowledge quiz
Check your understanding before marking the unit complete.
1. RelayOps designs a four-week pilot with one primary metric (median emergency dispatch time) and pre-registered success and kill thresholds. What is this experiment's core purpose?
2. Maya runs a concierge week where she manually routes five emergency jobs behind a Google Form intake before software ships. Which learning-loop principle does this apply?
3. RelayOps tracks three pilot sites staggered over four weeks each. Week-two median dispatch time is 11 minutes versus a 12-minute baseline. What is the best interpretation?
4. Jordan wants to change both onboarding copy and routing logic in the same two-week sprint. Why does the experiment design lesson advise against this?
5. RelayOps defines success as median dispatch under 5 minutes on 20+ live emergency jobs with 80% daily active dispatchers. Pilot A hits 4.8 minutes but only 55% daily active. What is the correct call?
6. A learning loop retro lists: hypothesis, metric, result, decision, next test. RelayOps's result is 'median 6.2 minutes, DAU 72%' after four weeks. What decision type fits a pre-committed kill rule of no improvement?
7. RelayOps spends two weeks per learning cycle. Over a 12-week quarter with one cycle at a time, how many full experiments can complete if each needs two weeks of measurement after a one-week setup?
8. An investor asks RelayOps to run paid LinkedIn ads during the pilot to 'accelerate learning.' Why is this usually a poor experiment trade-off pre-adoption?