Average Conversion Rate Uplift
12.7%
The average improvement seen in winning A/B test variations across e-commerce.
95%
Standard Confidence Level to Declare a Winner
2-4 Weeks
Typical Duration for a Reliable A/B Test
1,000+
Recommended Conversions Per Variation
Z-Test
Most Common Test for Conversion Rates
Test Deep Dive: Choosing Your Statistical Tool
The right test depends on your data. Here’s a breakdown of the most common statistical tests and what they're best for.
Z-Test vs. T-Test
These two tests are the workhorses of A/B testing, used to compare two variations. The main difference lies in the type of data you're analyzing. Z-Tests are for proportions (like conversion rates), while T-Tests are for continuous averages (like session duration).
Chi-Squared Test: Analyzing Choices
When you want to see if there's a significant difference in how users are distributed across several categories (e.g., which plan they chose, which feature they used most), the Chi-Squared test is your tool. It compares the observed distribution to what you would expect by chance.
ANOVA: Testing More Than Two Variations
Running an A/B/n test with three or more versions? Analysis of Variance (ANOVA) tells you if there's a statistically significant difference somewhere among the groups. It's a great way to test multiple ideas at once, but requires follow-up tests to find the specific winner.
The A/B Test Workflow
A successful test is more than just code; it's a rigorous process from start to finish.