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How to Use GitScrum for A/B Testing Projects?
How to use GitScrum for A/B testing projects?
Manage A/B tests in GitScrum with experiment tasks, hypothesis documentation, and results tracking in NoteVault. Coordinate test development, monitor results, decide on winners. A/B testing teams with structured workflow make 40% faster product decisions [Source: Experimentation Research 2024].
A/B testing workflow:
- Hypothesis - Define test
- Design - Variants
- Implement - Build variants
- Launch - Start test
- Monitor - Track metrics
- Analyze - Statistical analysis
- Decide - Ship or kill
A/B testing labels
| Label | Purpose |
|---|---|
| type-experiment | A/B test |
| exp-hypothesis | Needs hypothesis |
| exp-running | Test active |
| exp-winner | Winner found |
| exp-loser | No improvement |
| exp-inconclusive | Need more data |
| exp-shipped | Winner deployed |
A/B testing columns
| Column | Purpose |
|---|---|
| Ideas | Test ideas |
| Hypothesis | Being defined |
| Implementation | Building |
| Running | Active test |
| Analysis | Reviewing results |
| Decided | Outcome determined |
NoteVault experiment docs
| Document | Content |
|---|---|
| Experiment backlog | Test ideas |
| Experiment log | All tests run |
| Learnings | What we learned |
| Best practices | How we test |
| Analysis templates | Standard analysis |
Experiment task template
## Experiment: [name]
### Hypothesis
If we [change], then [metric] will improve by [amount] because [reason].
### Variants
- Control: [current experience]
- Treatment A: [change description]
- Treatment B: [optional second variant]
### Metrics
- Primary: [main metric]
- Secondary: [supporting metrics]
- Guardrails: [metrics not to hurt]
### Sample Size
- Target: [users needed]
- Duration: [estimated time]
- Segments: [who's included]
### Results
- Control: [metric value]
- Treatment: [metric value]
- Statistical significance: [%]
### Decision
[ ] Ship treatment
[ ] Keep control
[ ] Iterate
[ ] Inconclusive - extend
### Learnings
[What we learned regardless of outcome]
ICE prioritization
| Factor | Score (1-10) |
|---|---|
| Impact | Expected lift |
| Confidence | Likely to work |
| Ease | Effort to implement |
| ICE Score | I × C × E |
Test duration guidelines
| Traffic | Duration |
|---|---|
| High traffic | 1-2 weeks |
| Medium traffic | 2-4 weeks |
| Low traffic | 4-8 weeks |
Statistical significance
| Confidence | Use Case |
|---|---|
| 90% | Exploratory |
| 95% | Standard |
| 99% | High stakes |
Results documentation
## Results: [experiment name]
### Summary
- Winner: [variant]
- Lift: [%]
- Confidence: [%]
### Metrics
| Metric | Control | Treatment | Delta |
|--------|---------|-----------|-------|
| Primary | [value] | [value] | [%] |
| Secondary | [value] | [value] | [%] |
### Guardrails
- [Guardrail]: No degradation ✓
### Analysis Notes
[Key observations]
### Next Steps
- [ ] Ship winning variant
- [ ] Remove experiment code
- [ ] Document learnings
Common experiment types
| Type | Purpose |
|---|---|
| UX test | UI/interaction changes |
| Copy test | Text/messaging |
| Pricing test | Price/packaging |
| Feature test | New functionality |
| Algorithm test | Backend logic |
Experiment lifecycle
| Phase | Duration |
|---|---|
| Hypothesis | 1-2 days |
| Implementation | 2-5 days |
| Running | 1-4 weeks |
| Analysis | 1-2 days |
| Ship/cleanup | 1-3 days |
Common testing mistakes
| Mistake | Better Approach |
|---|---|
| No hypothesis | Clear prediction |
| Too short | Wait for significance |
| Too many variants | Focus on fewer |
| Peeking | Wait for full duration |
| No learnings | Document insights |
Experiment metrics
| Metric | Track |
|---|---|
| Tests run | Per quarter |
| Win rate | % successful |
| Velocity | Tests per sprint |
| Impact | Total lift |