In This Article
Introduction
There are several approaches to experimental testing, each with different strengths and use cases. Understanding when to use each type helps you design more effective experiments and get answers faster.
A/B Tests (Split Tests)
Description
Compare two versions: a control (A) and a single variant (B). Traffic is split evenly between versions.
Characteristics
- Simplest test type - easy to implement and analyze
- Clear winner - straightforward comparison
- Lower traffic requirements than multivariate
- Tests one change at a time
Best For
- Testing a single significant change
- When you have limited traffic
- When you need clear, actionable results
Example
Testing whether a red or blue "Buy Now" button converts better.
A/B/n Tests
Description
Compare multiple variants (A, B, C, D...) simultaneously. Traffic is split among all versions.
Characteristics
- Tests multiple ideas at once
- Requires more traffic than A/B (split among more groups)
- Risk of false positives increases with more variants
- Faster than sequential A/B tests
Best For
- Testing several distinct alternatives
- When you have high traffic
- Early exploration of ideas
Multivariate Tests (MVT)
Description
Test multiple elements simultaneously in all possible combinations to understand both individual and interaction effects.
Example
Testing 2 headlines × 2 images × 2 button colors = 8 combinations
Characteristics
| Pros | Cons |
|---|---|
| Tests interactions between elements | Requires very high traffic |
| Finds optimal combination | Complex to set up and analyze |
| Tests multiple elements at once | Takes longer to reach significance |
| More insights per test | May be hard to interpret results |
Best For
- High-traffic pages where interactions matter
- Landing page optimization
- When you suspect elements interact
Multi-Armed Bandits
Description
An adaptive algorithm that automatically shifts traffic toward better-performing variants during the test, balancing exploration (learning) with exploitation (maximizing conversions).
Characteristics
- Reduces opportunity cost - less traffic to losing variants
- Adapts in real-time - traffic allocation changes
- Always learning - can adapt to changing conditions
- No fixed endpoint - continuous optimization
Trade-offs vs Traditional A/B
| Aspect | A/B Test | Multi-Armed Bandit |
|---|---|---|
| Traffic allocation | Fixed (50/50) | Dynamic (shifts to winner) |
| Statistical rigor | High | Lower |
| Opportunity cost | Higher | Lower |
| Clear endpoint | Yes | No |
| Best for | Learning | Optimizing revenue |
Best For
- When opportunity cost matters (e.g., promotional campaigns)
- Continuous optimization
- Personalization engines
Choosing the Right Approach
| Situation | Recommended Approach |
|---|---|
| Testing one change, need clear answer | A/B Test |
| Several distinct ideas to compare | A/B/n Test |
| Multiple elements, high traffic | Multivariate Test |
| Short campaign, revenue matters | Multi-Armed Bandit |
| Low traffic, limited time | A/B Test (one variant) |
Conclusion
Key Takeaways
- A/B tests are simple and require least traffic
- A/B/n tests compare multiple variants simultaneously
- Multivariate tests reveal interaction effects but need high traffic
- Multi-armed bandits reduce opportunity cost but sacrifice rigor
- Choose based on traffic, goals, and statistical needs
- More variants = more traffic required
- Start simple with A/B tests until you have significant traffic