What characterizes Non-Parametric Data?

Non-parametric data is characterized by the absence of assumptions regarding the underlying distribution of the data. Unlike parametric data, which is expected to adhere to certain distribution patterns (such as normal or Gaussian distribution), non-parametric data can take on a variety of forms and does not require any distributional assumptions. This flexibility makes non-parametric methods particularly useful in analyzing data that do not conform to typical distributions or when sample sizes are small.

For example, when dealing with ordinal data, categorical data, or even continuous data that do not fit known distributions, researchers often use non-parametric statistical methods to draw conclusions without the constraints imposed by parametric approaches. As a result, the option correctly highlights the defining trait of non-parametric data, which is that it does not fit a known or well-understood distribution, allowing for broader application across diverse datasets.