Running A/B tests is easy. Running A/B tests that produce reliable, actionable results is surprisingly hard. The gap between the two is occupied by methodological errors so common, so invisible, and so costly that addressing them is often the single highest-leverage improvement available to a growth team.
Mistake 1: Testing Multiple Changes at Once
Changing the headline, CTA color, hero image, and pricing layout simultaneously isn't an A/B test; it's a comparison of two different pages. When the variant wins (or loses), you have no idea which change drove the result. You've generated a fact while destroying the context needed to make it useful. Isolate one variable at a time, or use a multivariate framework with appropriate sample sizes.
Mistake 2: Stopping Tests When Results Look Good
Checking results daily and stopping at first significance is the peeking problem. The false positive rate for a test stopped at first significance can exceed 25%. Pre-commit to a sample size target and minimum runtime before you launch, and don't call results until both are met.
Mistake 3: Ignoring Segment-Level Data
Aggregate results mask dramatically divergent outcomes by segment. A test where desktop visitors prefer variant A while mobile visitors strongly prefer variant B may declare A the winner, effectively shipping a change that harms your mobile audience. Always analyze by device type, traffic source, and new vs. returning visitors.
Mistake 4: Only Testing Big, Dramatic Changes
The case studies shared on marketing blogs are survivorship bias in action. The vast majority of high-value optimization comes from small, targeted changes to high-volume pages: a CTA three words shorter, a removed form field, a trust signal moved above the fold. These tests run faster, reach significance sooner, and compound into significant lifts over time.
Mistake 5: Never Writing Down What You Learned
An experiment whose results were never documented might as well not have run. Organizational memory is fragile. The marketer who ran the test leaves. The PM who interpreted it moves to another team. Without records, the same hypotheses get retested and the same errors get repeated. Every experiment (including null results) deserves a written record of hypothesis, methodology, result, and interpretation.
“The value of an experimentation program is not in any single test. It's in the institutional memory those tests create, if you bother to write it down.”
Segmently