A/B testing is one of the most powerful tools in a product team's arsenal, but only when done correctly. This guide covers everything you need to know to run effective experiments.
Why A/B Testing Matters
In a world of opinions, A/B testing provides objective evidence. Rather than debating whether version A or version B is better, you can simply test both and let user behavior decide.
### The Cost of Not Testing
Every change you make to your product is a bet. Without testing, you're betting blind. Some changes that seem obviously positive turn out to hurt key metrics.
Experimental Design
Good experiments start with careful design. Rushing into a test without proper planning leads to inconclusive results.
### Start with a Hypothesis
Every experiment should begin with a clear hypothesis: "If we [make this change], then [this metric] will [increase/decrease] by [amount] because [rationale]."
### Choose the Right Metrics
Select a single primary metric that the experiment will be evaluated against. Track secondary metrics to understand broader impact and catch unintended consequences.
Sample Size and Duration
Running tests with insufficient sample size is one of the most common mistakes in A/B testing.
### Calculating Sample Size
Sample size depends on baseline conversion rate, minimum detectable effect, statistical power, and significance level. Use statistical calculators to determine appropriate sample sizes.
Common Pitfalls
Even experienced teams make mistakes with A/B testing:
- **Testing too many variants**: Stick to two variants unless you have very high traffic - **Changing tests mid-flight**: Once launched, don't modify tests - **Ignoring segment effects**: Overall results can mask significant segment differences - **Peeking at results**: Resist the urge to check results before the test is complete
Conclusion
A/B testing is a powerful tool for product optimization, but only when done correctly. Start with the fundamentals, avoid common mistakes, and build more sophisticated capabilities over time.
