A measure of whether an observed result is unlikely to have occurred by random chance under a defined statistical model.
In testing, it helps determine how confidently one variation appears to outperform another.
Short and sweet:
A measure of whether an observed result is unlikely to have occurred by random chance under a defined statistical model.
In testing, it helps determine how confidently one variation appears to outperform another.
See also: A/B Testing, Conversion Rate, Analytics
Statistical significance indicates whether a difference observed in test results, like one landing page outperforming another, is likely a real effect rather than something that happened by random chance alone. It’s typically expressed through a confidence level, such as 95%, which reflects how sure you can be that the result would hold up if the test were repeated. Ending a test too early, before enough data has accumulated, is one of the most common ways marketers mistake random noise for a meaningful finding.
When an e-commerce team runs an A/B test on a checkout button and one version converts better, they check for statistical significance before rolling it out, since a small early lead could easily be random noise.
Optimizely and similar testing platforms display a confidence percentage, typically flagging a result as significant only once it clears the standard 95 percent threshold researchers rely on.
A pharmaceutical trial testing a new drug against a placebo can’t claim the drug works until the improvement in patients passes this same bar, ruling out the chance that it was just a coincidence.
