How to interpret statistical significance in email tests?

Statistical significance tells you whether observed differences between test variants are likely real or just random noise. A result is typically considered significant at the 95% confidence level-meaning there's only a 5% chance the difference occurred by random chance. Without significance, declaring a \"winner\" is gambling: you might be acting on patterns that don't actually exist.

Significance depends on three factors: sample size (more data = more reliable conclusions), observed difference (larger differences are easier to confirm), and baseline conversion rate (small changes to low-rate metrics need huge samples to verify). If Version A has a 22% open rate and Version B has 21.5%, you need a very large sample to confirm that half-percent difference isn't noise. If A is 25% and B is 18%, significance comes faster.

Use significance calculators (many are freely available online) rather than eyeballing results. Input your sample sizes and conversion numbers; the calculator returns confidence levels. Don't stop tests early just because one variant looks ahead-premature conclusions are often wrong. Patience with testing methodology pays dividends: a properly significant result you can trust beats a quick answer that might be wrong.