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Ambiguous A/B test: B wins but you're unsure

A/B test ambiguo: vince B ma non sei sicuro · Data Analysis · Advanced

The case

An A/B test on the new checkout has run for 6 days. Variant B converts at 4.4% vs 4.1% for A, a +7% relative lift, with about 3,000 users per group. The PM wants to launch B to everyone now. The p-value is 0.08. A promotion ran mid-test affecting only some users. The signal is promising but not clean. How do you decide whether B truly wins or you risk launching on a noisy result?

Un A/B test sul nuovo checkout gira da 6 giorni. La variante B converte al 4,4% contro il 4,1% di A, un +7% relativo, con circa 3.000 utenti per gruppo. Il product manager vuole lanciare B subito su tutti. Il p-value è 0,08. C'è stata una promozione a metà test che ha toccato solo alcuni utenti. Il segnale è promettente ma non netto. Come decidi se B vince davvero o se rischi di lanciare su un risultato rumoroso?

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Frequently asked questions

What is the "Ambiguous A/B test: B wins but you're unsure" problem-solving case on Grace?

It is an advanced-level Data Analysis case based on a real workplace situation. You read the problem, write how you would solve it in free text, and Grace's AI judges whether your solution actually works, and tells you what is still uncovered.

Who is this case for?

Anyone who wants to train structured problem solving: managers, professionals and candidates preparing for case interviews in Data Analysis. The advanced difficulty makes it a demanding test of real-world judgment.

Can I retry if my solution doesn't solve the case?

Yes. The feedback tells you what remains uncovered, without revealing the solution, so you can strengthen your plan and try again. After you solve it, 6 more Data Analysis cases are waiting.

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