Single choice · #380
At an operations meeting, a manager reports: "The new version of the app has been live for a week, and the average daily usage time per user has increased from 20 minutes to 28 minutes. This shows the update was very successful." Which of the following is the most reasonable "alternative explanation" for this conclusion?
Show answer & explanation
Answer: D
- ANo matter how successful the update is, it cannot increase usage time for all users.✗ Incorrect: This is an absolute assertion and does not provide an alternative cause for the rise in usage time. It cannot replace "successful update" to explain the data, so it does not constitute an "alternative explanation."
- BThe measurement standards for average daily usage time may be flawed.✗ Incorrect: This questions the reliability of the evidence itself, which is the second step in the three-step argument check. An "alternative explanation" requires an alternative cause for the conclusion while assuming the evidence is true.
- CThe increase in usage time shows that the old version's design was entirely without merit.✗ Incorrect: This is irrelevant to whether the new version is successful. It only makes an extreme statement, neither supporting nor weakening the manager's conclusion, and does not explain why usage time increased.
- DIt happened to coincide with a holiday, giving users more free time, so the increase is unrelated to the update.✓ Correct: An external factor (holiday effect) can also explain the rise in usage time. This shows that even if the data is true, it may not be the update that caused the increase, which is precisely the kind of alternative explanation possible between evidence and conclusion.
Explanation:The third step of the three-step argument check is: even if the evidence is true, is it sufficient to support the conclusion, and are there alternative explanations? "The launch week coincided with a holiday" is a typical confounding factor—users having more free time could also lead to increased usage time, so the increase cannot be uniquely attributed to the update. Note the distinction: questioning data reliability (measurement standards) is checking the evidence, not proposing an alternative explanation; absolute assertions and irrelevant inferences are not explanations either.