A characteristic of nonparametric biostatistical tests is that the data is:

Nonparametric biostatistical tests are specifically designed for situations where the data does not meet the assumptions required for parametric tests. These assumptions often include the requirement of normality in the data distribution. Nonparametric tests are robust and can be applied to data that is ordinal or nominal, as well as continuous data that may not follow a normal distribution.

The essence of a nonparametric test is its flexibility with data types. By not assuming a normal distribution, these tests can handle a variety of data shapes, including skewed, bimodal, or those with outliers. This characteristic makes them particularly valuable when working with real-world data, which often deviates from theoretical distributions.

The other options reflect characteristics linked to parametric tests or specific data distributions not applicable to nonparametric tests. Thus, understanding that nonparametric tests are suitable for data that is not normally distributed is key to grasping their application in biostatistics.