Which type of data would require nonparametric statistical methods?

Nonparametric statistical methods are particularly useful for analyzing data that does not meet certain assumptions required by parametric methods, such as the assumption of a normal distribution. In this context, ordinal or interval data is indeed appropriate for nonparametric methods because it allows these methods to analyze data that may not be evenly distributed or where the scale of measurement does not satisfy interval properties.

Ordinal data represents categories with a meaningful order but without a consistent difference between categories, making it unsuitable for parametric tests that generally require interval data with specific statistical properties. Nonparametric tests are designed to analyze such types of data without assuming a specific distribution, making them flexible and applicable in a wider range of scenarios.

While nominal categorical data also does not conform to the requirements of parametric statistics, the context of the question specifically addresses the more nuanced characteristics of ordinal and interval data, indicating that nonparametric methods are sought for data types that have ranks or ordered categories. This is why ordinal or interval data is the correct answer in this scenario.