What can skew the results of an A/B test if not run long enough?

• A. The weather on a given day
• B. The device type used by visitors
• C. The color scheme of the page
• D. Seasonal sales promotions or unusual sales patterns

Answer: (D)

When you run an A/B test, you need enough time to average out normal swings in traffic and user behavior. Time-based effects, like seasonal promotions or unusual sales patterns, can cause big shifts in how many people visit and convert during certain periods. If the test ends before these cycles or events complete, you may end up measuring the impact of the promotion or anomaly rather than the change you’re testing. That’s why not running long enough can skew results—the observed difference might reflect a time-specific spike or dip rather than a true effect of the variant.

Weather on a single day or the mix of device types can introduce noise or bias, but they aren’t as directly tied to repeating time-based cycles as seasonal promotions are. The color scheme is part of the experiment itself and its effect isn’t primarily a timing issue. The key takeaway is to design the test duration to capture enough cycles and events so observed differences reflect the treatment, not temporary time-based factors.