A university claims that a new language program improves student fluency. The conclusion is that the program should be adopted.

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The main idea here is how trustworthy the evidence is for claiming the program should be adopted. If the claimed gains in fluency come from self-reported improvements, the evidence is vulnerable to bias. People may overestimate their progress, want to please evaluators, or respond in a way that reflects expectations rather than actual ability. Without objective measures—like standardized fluency tests, performance-based assessments, or a control group—the conclusion that the program truly boosts fluency isn’t well supported. Objective data are needed to separate real gains from perceived ones before recommending adoption. The other options raise generalizability or comparability issues, but they don’t address the core problem of relying on subjective self-reports to claim real improvement.