A/B Testing with Chi-Square: Measuring Checkout Flow Conversion Lift

Paragraph 1 — The Scenario

CartPulse is a growing U.S. e-commerce platform specializing in home goods and kitchenware, handling roughly 1,200 checkout attempts per week. Over the past two quarters, the product team noticed that a large share of customers were abandoning their carts at the payment step — a notoriously high-friction point in any online purchase journey. To address this, the engineering team redesigned the checkout experience: fewer form fields, a visible progress bar, and a more prominent call-to-action button. The product team is now ready to test whether this redesign actually improved the one metric that matters most — conversion rate.

Paragraph 2 — The Statistical Challenge

CartPulse ran a controlled experiment over two weeks. Visitors were randomly split into two groups: the Control group (600 users) saw the original multi-step checkout, and the Treatment group (600 users) saw the new streamlined design. For each user, the outcome was binary — they either completed the purchase (converted = 1) or abandoned the checkout (converted = 0). The raw counts show a higher conversion rate in the Treatment group, but the product manager needs more than a percentage comparison. You have been brought in as the analyst to determine whether the observed difference in conversion rates is statistically significant or simply the result of random variation between the two groups.

Paragraph 3 — What's at Stake

CartPulse processes approximately $180,000 in weekly revenue. Even a 3–5 percentage point improvement in checkout conversion would translate to tens of thousands of dollars in additional monthly revenue. But rolling out the new checkout to all users carries development and QA cost — and if the improvement is not statistically real, that investment is wasted. The results of this A/B test will directly determine whether the new checkout design gets a full production rollout, is sent back for further iteration, or is abandoned entirely. The decision must be backed by evidence.