In A/B testing, which statement best explains why you should test only one change or variable at a time?

• A. To speed up the testing process
• B. To reduce data collection
• C. To accurately determine which variable caused any change in performance
• D. To avoid measuring success metrics

Answer: (C)

When running A/B tests, the goal is to know whether a specific change actually caused the observed difference in performance. If you modify more than one variable at once, other changes can influence the outcome or interact with each other, creating confounding effects. That makes it impossible to tell which change drove the result. Testing a single change at a time keeps everything else constant, so any measured difference can be attributed to that exact change. If you need to study multiple variables, you’d use a more complex design that allows you to estimate individual effects and interactions, but for clear attribution, isolating one variable is essential. The other options aren’t the primary reason: speed, data collection, or skipping metrics don’t capture why isolation helps you determine causality.