What is a key characteristic of non-parametric statistics compared to parametric statistics?

Non-parametric statistics are characterized by their independence from specific assumptions about population parameters. This means they do not require the data to meet certain criteria, such as normality or homogeneity of variance, that parametric tests typically do. Instead, non-parametric methods are often used when the data do not conform to these assumptions or when the sample size is too small to validate parametric approaches. This flexibility allows researchers to analyze ordinal data or data that do not meet interval scale requirements.

The other options relate to aspects that do not accurately represent the core distinguishing feature of non-parametric statistics. Non-parametric tests can be used with smaller sample sizes and do not inherently possess greater power to reject null hypotheses than parametric tests; in fact, parametric tests are often considered more powerful when their assumptions are met. Additionally, non-parametric methods do not always require interval data, as they can be applied to ordinal data as well. Thus, the emphasis on not being based on population parameters is the defining trait of non-parametric statistics.