Statistical significance combines statistical evidence with practical business impact to validate test results. Like scientific research, solid validation looks at both mathematical proof through p-value (a number between 0 and 1) and real-world effects that matter for your business goals. A strong test shows both types of significance.^[1] 

Measuring statistical significance starts with p-value calculations. A p-value below 0.05 indicates a 95% confidence level that your results reflect real differences. Practical significance focuses on effect size — the magnitude of change your test created. For example, a 20% increase in conversion rate with a p-value of 0.03 shows both strong statistical proof and meaningful business impact.

Sample size and test duration form crucial parts of validation. Your test needs enough users and time to produce reliable data, but not so much that tiny differences appear meaningful. Calculate required sample size before starting, and plan your test duration to capture true behavior patterns. Remember that larger changes need fewer samples to validate, while subtle differences require more data to confirm.^[2]