Partitioning should be considered only if there are at least _______ individuals within each subclass or if there are clear clinical reasons.

• A. 20
• B. 30
• C. 40
• D. 50

Answer: (C)

When you partition data into subclasses, you must ensure enough observations in each group to estimate outcomes with reasonable precision and to avoid unstable, overfitted results. Having about 40 individuals in each subclass strikes a practical balance: it provides enough data to estimate group-specific proportions or means, and to obtain confidence intervals that aren’t excessively wide. With fewer than this, estimates become unstable and the model’s conclusions can be unreliable, especially as you create more subclasses or add covariates. If there are clear clinical reasons to justify a partition with smaller numbers, you might proceed, but in the absence of such justification, targeting around 40 per subclass is a sensible minimum. Choices with smaller numbers (e.g., 20 or 30) are typically underpowered for stable estimates, while a larger number (50) improves precision but isn’t necessary for the intended purpose.