Counselor Education Comprehensive Exam (CECE) Practice Exam

Parametric statistics are used when statistical assumptions are met. Which of the following IS NOT an example of parametric statistics?

1. T-test

2. Analysis of variance (ANOVA)

3. Analysis of covariance (ANCOVA)

4. None of the above

The correct answer is: None of the above

Parametric statistics refer to statistical techniques that make certain assumptions about the parameters of the population from which samples are drawn, such as normality and homogeneity of variance. The methods listed in the correct answer are designed based on these assumptions and typically involve interval or ratio level data. Analysis of covariance (ANCOVA) is a statistical technique that combines ANOVA and regression. It allows researchers to control for one or more continuous variables that may influence the dependent variable, thereby improving the accuracy of comparisons among groups. The key aspect of ANCOVA is that it relies on the assumptions of parametric statistics, including the distribution of the data. In contrast, if a particular method or test is non-parametric, it does not rely on these assumptions about the population parameters. Examples of non-parametric statistics include the Mann-Whitney U test, the Kruskal-Wallis test, or Chi-square tests. Understanding this distinction is important for correctly choosing statistical techniques in research. While all the other options listed are indeed examples of parametric tests, relying on certain assumptions about the underlying data, the right choice highlights a common misconception regarding the nature of ANCOVA when applied incorrectly in non-parametric contexts. Thus, the correct response is that ANCOVA is not an example