Which type of data analysis involves non parametric tests?

The correct response identifies non-parametric tests as those suited for data without an assumed distribution structure. Non-parametric methods are particularly useful when the data does not meet the specific requirements of parametric tests, such as the assumption of normality. This means that the data can take on any distribution without needing to conform to a specific model, making non-parametric tests more flexible for handling various data types.

Non-parametric techniques are often applied in situations where data is ordinal or when sample sizes are small and may not adequately represent a normal distribution. This characteristic allows researchers to analyze data without the constraints of having to verify if it follows any predetermined distribution criteria.

In contrast, data with a known distribution or that assumes a normal distribution aligns more closely with parametric tests, which rely on the data fitting certain models (e.g., t-tests, ANOVA). Tests based on mean comparisons specifically suggest a reliance on normal distribution characteristics, further distinguishing them from non-parametric approaches. Thus, option C accurately encompasses the principles behind non-parametric data analysis.