Statistical Significance

Statistical significance indicates whether an observed test result is likely due to a real effect rather than random variation. In marketing experiments, this helps teams decide if a change in conversion rate, click-through rate, or engagement is trustworthy enough to deploy broadly. Significance depends on sample size, variance, and confidence threshold; small samples can produce misleading swings that look meaningful but are noise. Proper interpretation requires looking at effect size and business impact alongside p-values. A statistically significant result with tiny practical lift may not justify implementation cost. Strong experimentation practice includes pre-defined success criteria, adequate test duration, guardrail metrics, and post-test validation to avoid false winners and unstable rollout decisions.