What is the potential drawback of declaring a winner in an A/B test based solely on the highest observed lift without considering statistical significance?

Question

Options

• AIt increases the risk of false negatives.
• BIt guarantees a 100% confidence level in the selected winner.
• CIt increases the likelihood of false positives.
• DIt ensures a more straightforward interpretation of test results.

• A
  6% (2)
• B
  6% (2)
• C
  76% (26)
• D
  12% (4)

Explanation

Declaring a winner based solely on the highest observed lift ignores whether the result could have occurred by chance, which inflates the false positive rate - you end up "detecting" an effect that isn't real, a Type I error. This is option C.

Why the distractors are wrong:

• A (false negatives): Ignoring statistical significance makes you more likely to accept weak results, not less - it reduces false negatives if anything, which is the opposite problem.
• B (100% confidence): Skipping significance testing does the opposite - it lowers your actual confidence, not raises it to certainty.
• D (simpler interpretation): While cherry-picking the highest lift might feel simpler, it produces unreliable conclusions, making real-world interpretation harder, not easier.

Memory tip: Think of it as "peeking at the scoreboard too early" - just because one team is briefly ahead doesn't mean they'll win. Statistical significance is the final whistle that confirms the lead is real, not random noise. No whistle = false positive risk.

No community discussion yet for this question.