A/B Testing Best Practices for Product Teams

A/B testing is essential for data-driven product decisions. This guide covers best practices for running experiments that deliver actionable insights.

Designing Good Experiments

Every experiment needs: - **Clear hypothesis**: What are you testing and why? - **Primary metric**: One key metric to measure success - **Sample size calculation**: Ensure statistical power - **Duration planning**: Account for weekly cycles

Common Mistakes

Avoid these pitfalls: - Stopping experiments too early - Testing too many variations at once - Ignoring statistical significance - Not accounting for novelty effects

Statistical Significance

Understanding significance: - 95% confidence is the standard threshold - Calculate required sample size before starting - Use proper statistical tests for your metric type

Interpreting Results

When analyzing results: - Look at confidence intervals, not just point estimates - Check for segment-level differences - Consider practical significance vs statistical significance - Document learnings for future experiments

Scaling Your Program

As your testing matures: - Build a hypothesis backlog - Create experiment review processes - Share learnings across teams - Invest in tooling and automation