Non-parametric methods are generally more robust than parametric methods.

Non-parametric methods are generally more robust than parametric methods.

• A. True
• B. False
• C. Sometimes
• D. Not enough information

Answer: (B)

Robustness is about how sensitive an method is to deviations from assumptions or to messy data. Non-parametric methods reduce reliance on a specific distribution, so they can be more forgiving when the data don’t meet parametric assumptions like normality. But robustness isn’t a one-size-fits-all trait. There are robust parametric approaches (for example, methods built around medians or robust estimators) that resist outliers and misspecifications just as well, or better, in certain situations. At the same time, some non-parametric procedures can be less robust to particular data issues or can suffer from inefficiency when the parametric model actually fits well. Because robustness depends on the context and the specific method, you can’t claim that non-parametric methods are generally more robust.

Robustness is about how sensitive an method is to deviations from assumptions or to messy data. Non-parametric methods reduce reliance on a specific distribution, so they can be more forgiving when the data don’t meet parametric assumptions like normality. But robustness isn’t a one-size-fits-all trait. There are robust parametric approaches (for example, methods built around medians or robust estimators) that resist outliers and misspecifications just as well, or better, in certain situations. At the same time, some non-parametric procedures can be less robust to particular data issues or can suffer from inefficiency when the parametric model actually fits well. Because robustness depends on the context and the specific method, you can’t claim that non-parametric methods are generally more robust.