How would you set up an A/B test for a new checkout flow?

What They’re Really Asking

Can you design a rigorous, bias-controlled experiment that demonstrates how you would validate a product change and make a data-driven decision?

Strong Sample Answer

I'd start by defining the hypothesis: the new checkout flow will increase conversion rate by at least 5% without negatively impacting average order value. I'd set the primary metric as checkout-to-purchase conversion rate, with secondary metrics like cart abandonment rate, time-to-complete, and error rate. To avoid novelty effects, I'd run the test for at least two full business cycles (typically two weeks). I'd segment users by device type (mobile vs desktop) and new vs returning users, since behaviors differ. Using a power analysis with a 95% confidence level and 80% power, I'd calculate that we need about 100,000 users per variant (assuming 10% baseline conversion). I'd randomly split 50/50, ensuring no overlap with other tests. During the test, I'd monitor for data quality issues like sample ratio mismatch. After the test, I'd use a two-sample z-test for conversion rate and consider Bayesian methods for expected loss. In my last role at a fintech company, we ran a similar test that improved conversion by 8% with p<0.001, and we scaled it to all users. I'd also check segment-level effects to ensure no negative impact on high-value customers.

Common Mistake to Avoid

Company-Specific Variants