Case context: You lead the machine learning team for a hospital network's triage classifier. The current system already performs well. A researcher proposes a new model that they estimate would improve accuracy by 0.02 percentage points, but they note that a development set of 5,000 patient cases is not large enough to verify such a small change.

Question: Based on the principle being taught, should you spend time evaluating this improvement, and what must change about the development set if you want to test it properly?

Sample answer: Yes. In a mature, high-impact application such as medical triage, even a very small gain can matter because it may affect safety, staffing, and operating cost. To measure that gain reliably, you need a much larger development set than 5,000 examples.

Key points:

• Small measured gains can still be valuable in high-impact systems.
• The practical impact can justify evaluating a tiny improvement.
• A larger development set is needed to detect such a small difference.

Rubric: The student must recognize that the proposed improvement is worth evaluating because of its importance in a mature, high-stakes application, and conclude that the development set must be increased well beyond 5,000 examples.