Which approach best supports isolating the effect of a single variable in A/B testing?

• A. Test only one change or variable at a time
• B. Change multiple variables at once
• C. Randomize without control
• D. Avoid any changes

Answer: (A)

Isolating the effect of a single variable in an A/B test relies on changing only one element between the two variants so that any difference in outcomes can be attributed to that specific change. If you modify multiple variables at once, interactions between those changes can occur, making it impossible to tell which variable caused the observed result. By keeping everything else constant and comparing a single change to the baseline, you get a clean, interpretable difference.

Randomization helps ensure the two groups are comparable, so observed differences aren’t due to who ends up in which group, but it doesn’t replace the need for a single, isolated change. Having no changes provides no learning about what works, and randomizing without a control means there’s no baseline to compare against. So testing only one change at a time best supports isolating the effect of that single variable.