What is a characteristic of non-parametric statistical methods?

The correct choice highlights a key characteristic of non-parametric statistical methods, specifically that they generally require a larger sample size compared to parametric methods. Non-parametric methods do not assume a specific distribution for the data, which allows them to be applied in a wider range of scenarios, particularly when dealing with small sample sizes or when the underlying data distribution is not normal. However, larger sample sizes can help to ensure the robustness of the results obtained through these methods.

Parametric tests, on the other hand, make specific assumptions about data distribution (usually normal distribution) and often perform well with smaller sample sizes since they leverage these assumptions. In contrast, non-parametric tests tend to be less powerful than parametric tests when the assumptions of parametric tests are met, which means that larger sample sizes can compensate for this lower power in obtaining reliable results.

This characteristic allows non-parametric methods to be more applicable in studies with diverse data types and distributions. They are valuable in research scenarios, particularly in clinical investigations like those typically encountered in Maternal-Fetal Medicine, where data may not always conform to parametric assumptions.