A/B Testing
Comparing two versions of something to determine which performs better using statistical analysis.
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Definition
A/B testing (split testing) is an experimental method that compares two versions of a variable (webpage, email, feature) to determine which performs better. Users are randomly split between variants, and statistical analysis determines if the difference is significant.
Proper A/B testing requires: a clear hypothesis, sufficient sample size for statistical significance (typically 95% confidence), one variable changed at a time, and enough time to account for daily/weekly patterns.
A/B testing can be applied to virtually anything: headlines, CTAs, pricing pages, onboarding flows, email subject lines, ad creatives, feature designs, and even business models. The companies that test most aggressively tend to optimize fastest.
Why It Matters for Founders
A/B testing removes guesswork from decision-making. Instead of debating which headline is better, you test both and let data decide. This evidence-based approach compounds — small improvements from dozens of tests add up to transformative changes.
For startups, A/B testing the right things can dramatically impact growth. A 20% improvement in landing page conversion rate (from testing headlines, CTAs, and layout) effectively reduces CAC by 20% — equivalent to finding a 20% cheaper acquisition channel.
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Real-World Example
Testing two homepage headlines: A ("Discover the best startup tools") vs. B ("Find your next favorite product"). After 10K visitors per variant, B converts at 4.2% vs A's 3.1% with 95% confidence.
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Frequently Asked Questions
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