Case context: A data scientist builds artificial examples to enlarge a dataset of customer-support emails. People reviewing a sample of the generated messages say they look natural and believable. Yet a classifier trained with these synthetic emails performs poorly on a held-out set, which suggests the generation process did not preserve the patterns the model needs.

Question: What is the most likely problem with the generated data, and how should the scientist change the way the data is checked before using it for training?

Sample answer: The generated examples may be convincing to people but still fail to match the statistical structure that matters to the learning algorithm. In other words, the data has surface-level plausibility without being reliable from the model's point of view. The scientist should strengthen validation so it tests whether the synthetic examples behave like the real data for downstream learning, rather than relying only on human review.

Key points:

• Recognize that a dataset can seem authentic to reviewers yet still be unsuitable for training.
• Explain that the problem is a mismatch between human judgment and model-facing behavior.
• Update validation to include checks based on how a learner responds to the synthetic data, not just visual or manual inspection.

Rubric: The response should identify that the synthetic data is believable to people but not realistic for the model, and state that validation must move beyond human inspection to assess whether the data supports the intended learning task.