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. Bivariate regression

The correct answer is: Analysis of covariance (ANCOVA)

Parametric statistics are employed when the data meet specific assumptions, which generally include normality, homogeneity of variance, and interval or ratio scale measurement. Each of the mentioned statistical tests has characteristics that classify them as parametric. Analysis of covariance (ANCOVA) is indeed a parametric statistical technique that combines features of both ANOVA and regression. It is utilized to compare one or more means while controlling for other variables that could interfere with the results. It assumes the data meets the typical parametric conditions, hence it does not fit the description of being "not an example of parametric statistics." On the other hand, the T-test, ANOVA, and Bivariate regression are all classic parametric methods that rely on those same statistical assumptions. The T-test compares means between two groups, ANOVA extends this by allowing comparisons among three or more groups, and Bivariate regression examines relationships between two continuous variables. In contrast to the options listed, non-parametric statistics would be used when the assumptions of normality or homogeneity of variance are compromised, or when dealing with ordinal data. An example of a non-parametric test might include the Mann-Whitney U test or the Kruskal-Wallis H test.