Two-Proportion Z-Test
Hypothesis Testing
Comparing Rates Across Groups
The two-proportion z-test evaluates whether two groups have different success rates, forming the statistical backbone of A/B testing. It provides a rigorous framework for comparing binary outcomes.
- Digital Marketing — Testing whether landing page designs produce different conversion rates
- Medicine — Comparing treatment success rates in randomized clinical trials
- Social Policy — Evaluating whether interventions reduce rates in different populations
The two-proportion test turns business questions into statistical evidence.
Tests whether two population proportions are equal: H₀: π₁ = π₂.
where is the pooled proportion.