A/B testing empowers product managers to make data-driven decisions by comparing different product versions. Knowing which test type to use is crucial for gathering meaningful insights.

Common A/B test types include:

• Standard A/B tests: Split traffic evenly between two variants to determine which performs better. This is the simplest form of experimentation.
• Feature tests: Evaluate new functionalities before full development. This pre-launch testing validates hypotheses about user interactions with potential features.
• Live tests: Compare variations of an existing product that's already available to users. These tests optimize elements of your product to improve conversion, engagement, or retention.
• Multi-armed bandit (MAB) tests: Use machine learning to dynamically adjust traffic distribution, automatically directing more users toward better-performing variants during the test. This balances learning with immediate optimization.
• Multivariate tests: Examine multiple variables simultaneously to understand how different elements interact. These require larger sample sizes but provide more nuanced insights.

When choosing a test type, consider:

• Your available user traffic
• The complexity of what you're testing
• How quickly you need results
• The importance of statistical certainty^[1]