One-Way ANOVA: Comparing Average Order Value Across Device Types

The Scenario

ShopWave is a mid-sized U.S. e-commerce platform selling consumer electronics and lifestyle products, processing over 150 orders daily. The Head of Growth has noticed a recurring pattern in the weekly sales dashboard: customers appear to spend different amounts per order depending on whether they shop on a mobile phone, a desktop browser, or a tablet. While the difference seems visible in the numbers, the business team is unsure whether it reflects a real and consistent behavioral pattern or simply the natural ups and downs of daily sales volume. Before making any budget decisions, leadership wants a clear, evidence-based answer.

The Statistical Challenge

A random sample of 150 recent orders has been collected — 50 from each device type (Mobile, Desktop, Tablet) — with the order value recorded for each transaction. When you glance at the group averages, desktop users appear to spend more, mobile users appear to spend less, and tablet falls somewhere in the middle. But averages from samples always fluctuate. You have been brought in as the analyst to determine whether these observed differences across all three groups are statistically significant, or whether they are simply the result of random variation. This is a classic multi-group comparison problem, and one-way ANOVA is the right tool for the job.

What's at Stake

The results of this analysis will directly shape how ShopWave allocates its $200,000 marketing and UX budget for the upcoming quarter. If desktop users genuinely place higher-value orders on average, the team will prioritize desktop UX enhancements and invest in desktop-targeted campaigns for high-ticket products. If the differences are not statistically meaningful, no budget will be redirected and resources will stay focused on mobile-first optimization — which already accounts for 60% of total traffic. Statistical evidence, not intuition, will drive this decision.