2 min read
A/B testing vs A/B/n: when to use each
Testing more than two versions at once is tempting, but every extra variant splits your traffic. Learn when A/B/n pays off and when classic A/B wins.
Practical guides on experimentation, personalization, statistics and AI agents, written by the team that builds Bixlow and runs CRO programs every week.
2 min read
Testing more than two versions at once is tempting, but every extra variant splits your traffic. Learn when A/B/n pays off and when classic A/B wins.
2 min read
A poorly run A/B test is worse than no test at all: it gives you false confidence. Here are the most common mistakes that invalidate results and how to prevent them.
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Sample size quietly ruins more experiments than any other factor. Learn the three variables behind it and how to know if your A/B test can actually reach a conclusion.
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Stop too early and you crown false winners; run too long and you waste traffic. Learn how to set the right A/B test duration before you launch.
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P-value, confidence level and false winners explained in plain English. Learn to read what your A/B testing tool tells you and stop shipping fake wins.
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A clear, testable hypothesis is the difference between an experiment that teaches you something and one that just burns traffic. Here's how to write them.
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Learn the core A/B testing vocabulary: control, variant, hypothesis, significance and metrics, so you can launch reliable experiments from day one.
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More ideas than you can run at once? Learn a simple framework to rank your experiment backlog by impact, confidence and effort, and start with what pays off most.
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When marketing wants to ship changes without going through dev, engineering pictures chaos in production. A separate experimentation layer makes that fear unfounded.
2 min read
Five proven conversion levers you can ship today with no code and no deploy: hero headline, CTA, social proof, exit offer and traffic-source personalization.