A data scientist analyzed two versions of a web page with a baseline claim that there is no difference in clicks between them. The results suggested a difference, but further evaluation revealed that both versions performed at similar levels. Which choice best describes this mistake?
It was caused by large variation in the data
It is a Type I error
It is a Type II error
It is a valid result supported by sufficient evidence
The scenario describes selecting a difference when none exists, which is known as a Type I error. Failing to identify a real difference, a Type II error, does not match the tester’s conclusion. A high standard deviation does not alone explain why the test showed an incorrect difference. Calling the result valid contradicts the later findings that revealed no actual difference.
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How can large variations in data affect hypothesis testing?