Testing too many variables at once?

Testing multiple variables simultaneously creates confounding that makes results uninterpretable:

The problem: If you change subject line, images, and CTA between variants, you cannot know which change (or combination) caused any performance difference.

Attribution impossible: Did the new subject line help? Did the new image hurt? Did they cancel each other out? You cannot answer these questions.

False conclusions likely: You might attribute success to the subject line when it was actually the image, leading to wrong future decisions.

Solutions:

Test one variable at a time through sequential A/B tests. If you must test multiple variables simultaneously, use proper multivariate methodology with adequate sample sizes.

Isolation enables learning. Without it, you have results but no understanding of what caused them.