Common A/B Testing Mistakes to Avoid

What You’ll Learn

You’ll identify the most frequent errors that undermine A/B testing validity, from statistical blunders to procedural oversights. Learning these mistakes upfront protects your testing program and ensures your early experiments produce reliable results rather than false conclusions. The A/B Test Starter curriculum emphasizes error prevention because a single flawed test can mislead your entire team and waste months on the wrong changes.

Key Concepts

Many teams make predictable mistakes that invalidate their A/B testing efforts: stopping tests early when results look promising, failing to randomize traffic between versions, changing the test hypothesis mid-experiment based on preliminary data, or running too many simultaneous tests and confusing which variable caused which outcome. These errors stem from impatience, overconfidence, or lack of process discipline rather than inability to run tests technically. The A/B Test Starter teaches you to recognize these pitfalls and implement safeguards that keep your experimentation program honest and productive. Avoiding these mistakes requires discipline, but the payoff is that your testing program becomes a reliable source of truth for your business.

• Peeking and Early Stopping: Checking your test results before reaching your predetermined sample size introduces bias because you’re more likely to stop when early results favor one version, giving that version an unfair advantage. Commit to your sample size target and duration beforehand, then honor it.
• Multiple Comparison Problem: Running many tests simultaneously on the same page without adjustment increases false positives because each test has a 5% chance of being wrong, and those errors compound. The A/B Test Starter recommends running one primary test per page and using statistical corrections if you must run multiple tests.
• Unequal Traffic Split: If you accidentally allocate 70% of visitors to one version and 30% to another, you’re not running a controlled experiment anymore. Always ensure your testing tool splits traffic equally unless you have a specific statistical reason for an unequal split.
• Changing Hypotheses Mid-Test: Looking at preliminary results and deciding to test something different partway through is a form of p-hacking that inflates false positives. Write your hypothesis before the test starts and stick with it, then use preliminary insights to inform your next test.

Practical Application

Create a testing checklist for yourself that includes: (1) Calculate sample size and test duration before launching; (2) Set your hypothesis and success metric in writing before traffic starts; (3) Commit to not checking results daily; (4) Verify your testing tool is splitting traffic 50/50. Use this checklist for your first real experiment to build the discipline that separates successful testing programs from chaotic ones.