Start here · Essential guide
A Beginners Guide to A/B Testing
Learn how A/B testing works, from forming a hypothesis and splitting traffic to reading results and deciding what to do, with a simple worked example.
Conversion and Testing
A/B Testing
Running fair experiments and reading results honestly.
A/B Testing vs Multivariate Testing
Understand the difference between A/B and multivariate tests, how traffic needs change, and when testing several page elements at once is worth it.
Common A/B Testing Mistakes
Avoid the testing pitfalls that produce false winners: peeking at results, stopping early, testing too many things, ignoring seasonality and more.
How Much Traffic Do You Need to Run a Test
Why small samples give unreliable A/B test results, what drives the traffic you need, and practical options for sites with low visitor numbers.
How to Record and Learn from Past Tests
Set up a simple shared testing log so every experiment's hypothesis, result and decision is saved, and results build into knowledge about your audience.
How to Write a Testable Hypothesis
Turn a hunch into a specific, measurable prediction with a simple template, good and bad examples, and tips for choosing a metric and a clear success rule.
Testing Email Subject Lines Fairly
How to set up a fair split test of email subject lines, pick a metric that reflects real interest, and avoid reading too much into small differences.
What Statistical Significance Means for Marketers
A plain explanation of statistical significance, chance, confidence and p-values, and why early A/B test results can mislead even when they look convincing.
What to Test First on a Small Website
A simple scoring method for ranking test ideas by likely impact and effort, plus where small sites should look first for changes worth testing.
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