A campaign designed to improve newsletter signups has underperformed. Upon review, it was found that the call-to-action (CTA) text was unclear. How should the test hypothesis be updated?

Question

Options

• A"Updating the CTA text will increase newsletter signups by 20%."
• B"Changing the page layout will increase user engagement."
• C"Adjusting the newsletter content will reduce bounce rates."
• D"Reducing page load time will increase page views."

• A
  83% (25)
• B
  3% (1)
• C
  10% (3)
• D
  3% (1)

Explanation

Option A is correct because a well-formed test hypothesis must directly address the specific problem identified - in this case, the unclear CTA text - and pair it with a measurable outcome tied to the original campaign goal (newsletter signups). Option B is wrong because changing page layout doesn't address the CTA text issue found in the review, and "user engagement" is too vague to measure. Option C is wrong because it shifts focus to newsletter content and bounce rates, neither of which was identified as the root cause. Option D is wrong because page load time is entirely unrelated to the CTA clarity problem and targets a different metric altogether.

Memory tip: Use the phrase "Fix what's broken, measure what matters." The hypothesis must name the identified flaw (unclear CTA) as the variable and connect it to the original goal (signups) - if either half is missing or mismatched, the hypothesis is off-target.

No community discussion yet for this question.