A/B Test Results Interpretation with Statistical Rigor
Interpret A/B test results with awareness of significance, power, and effect size, for product and growth managers, without declaring an early winner or fabricating figures.
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Model: claude-sonnet-4-7
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Knowledge: 2026-Q2
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Evaluating a landing-page test before adopting the winner, reviewing a price or UI experiment before rollout, judging an email-campaign test before scaling it.
#اختبار A/B#دلالة إحصائيّة#حجم الأثر#تجارب التحويل
Built-in quality guards
Anti-hallucination
No fabricated p-value, confidence interval, or uplift percentage
Sources
Every number comes from the user's actual test results
Completeness
Cover design validity, significance, power, effect size, and decision
Format Check
Adherence to the results table and recommendation line