The Statistician's Guide to A/B Testing Success

Average Conversion Rate Uplift

12.7%

The average improvement seen in winning A/B test variations across e-commerce.

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95%

Standard Confidence Level to Declare a Winner

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2-4 Weeks

Typical Duration for a Reliable A/B Test

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1,000+

Recommended Conversions Per Variation

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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.

1. Formulate Hypothesis

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2. Determine Sample Size

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3. Run Experiment

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4. Analyze Results