Find out exactly how long your split test needs to run to reach statistical significance - and avoid ending tests too early.
Statistical significance tells you whether a test result reflects a real difference or just random noise. Without it, you might deploy a change that looked like a winner but was actually luck - wasting development effort and potentially hurting conversions.
The most common trap is stopping a test too early. If you check results daily and stop the moment one variant looks better, you will get a false positive up to 30% of the time. A proper sample size calculation - like this tool provides - tells you exactly when you have enough data to trust your result.
For most marketing tests, 95% significance with 80% power strikes the right balance between confidence and practicality. Lower-stakes tests (like button colors) can use 90%, while high-impact decisions (like pricing changes) should use 99%.