Which of the following does NOT apply to parametric statistics?

Parametric statistics are based on certain assumptions about the data, primarily concerning its distribution. One of the key features of parametric statistics is that they typically require the data to follow a normal distribution. This is foundational because many parametric tests, such as t-tests and ANOVAs, are designed to analyze data that adheres to this assumption, allowing for valid inferences about population parameters.

Parametric methods also assume that the sample data is drawn from a specific population, emphasizing the connection between the sample and the population it represents. Furthermore, these methods often rely on fixed parameters, which means they estimate certain characteristics of the population (like mean and variance) based on sample data.

On the other hand, nominal data is a type of categorical data that does not have a meaningful order or ranking, and it does not meet the assumptions required for parametric statistics. Thus, parametric tests are not applicable to nominal data, as they require measurements on an interval or ratio scale where numerical operations (like averaging) are meaningful. Therefore, the statement that parametric statistics can be used with nominal data does not apply and is accurate.