Which of the following is a best practice when implementing an experiment?
Start with multiple hypotheses.
Test one variable for each experiment.
Assign two campaigns to each experiment arm.
Divide your audience into as many arms as possible.
Explanation
Analysis of Correct Answer(s)
- Test one variable for each experiment: This is the core principle of effective A/B testing. By changing only one variable at a time (such as a headline, bidding strategy, or landing page), you can accurately attribute any performance difference to that specific change. This method provides clear, actionable insights and eliminates confusion about what caused the results. If you change multiple variables, you won't know which one was responsible for the success or failure of the experiment.
Analysis of Incorrect Options
- Divide your audience into as many arms as possible: Splitting your audience into too many groups (arms) dilutes your data. Each arm receives less traffic, making it harder and slower to reach statistical significance. For reliable results, it's better to focus on a limited number of variations.
- Assign two campaigns to each experiment arm: An experiment arm represents a single version of your campaign. The standard structure is one control arm (your original campaign) and one or more treatment arms (the variations you are testing). Assigning multiple campaigns to a single arm complicates the structure and invalidates the test.
- Start with multiple hypotheses: A strong experiment is designed to test a single, clear hypothesis. For example, "Will a 'Smart Bidding' strategy increase conversions compared to 'Manual CPC'?" Trying to test multiple hypotheses simultaneously makes it impossible to isolate the cause of any performance changes.