Nonparametric methods are typically chosen because:

Nonparametric methods are typically chosen because:

• A. They make fewer assumptions and can be more robust.
• B. They assume a normal distribution.
• C. They require large samples.
• D. They always outperform parametric methods.

Answer: (A)

The idea being tested is that nonparametric methods are chosen because they impose fewer assumptions and are more robust to deviations from ideal conditions. These methods don’t rely on a specific distribution like normality and often use data ranks rather than exact values. That makes them less sensitive to outliers, skewed or heavy-tailed distributions, and unequal variances, and allows them to handle ordinal data as well. Because of this flexibility, they’re especially useful when you’re unsure about the population distribution or you have a small sample. It’s true that when parametric assumptions do hold, those methods can be more powerful, and nonparametric methods don’t always outperform them.

The idea being tested is that nonparametric methods are chosen because they impose fewer assumptions and are more robust to deviations from ideal conditions. These methods don’t rely on a specific distribution like normality and often use data ranks rather than exact values. That makes them less sensitive to outliers, skewed or heavy-tailed distributions, and unequal variances, and allows them to handle ordinal data as well. Because of this flexibility, they’re especially useful when you’re unsure about the population distribution or you have a small sample. It’s true that when parametric assumptions do hold, those methods can be more powerful, and nonparametric methods don’t always outperform them.