Which of the following statements is true about parametric tests?

Parametric tests are statistical tests that rely on certain assumptions about the parameters of the population from which the samples are drawn. One of the key assumptions of parametric tests is the assumption of normality, which means that the data should be approximately normally distributed. This is important because many parametric tests, such as t-tests and ANOVA, use the mean and standard deviation as their underlying measures of central tendency and variability, which are influenced by the distribution of the data. When the assumption of normality is met, these tests can provide accurate and reliable results.

The other statements regarding parametric tests do not align with standard statistical principles. For example, parametric tests are not limited to discrete data; they are applicable to continuous data as well. They cannot handle any type of data distribution since they are specifically designed for normally distributed data. Finally, while outliers can have an impact on the results of parametric tests, these tests do not inherently provide robustness against the influence of outliers. Thus, the correct statement highlights the fundamental assumption of normality associated with parametric tests.