What tests does PROC UNIVARIATE provide for assessing normality in data distributions?

PROC UNIVARIATE in SAS provides various statistical tests for assessing the normality of data distributions, and one of the primary methods included in this procedure is the goodness-of-fit test. Specifically, PROC UNIVARIATE includes tests such as the Shapiro-Wilk test, the Kolmogorov-Smirnov test, and Anderson-Darling test, which are designed to evaluate whether the data conforms to a normal distribution.

These goodness-of-fit tests work by comparing the observed distribution of data to the expected distribution under the assumption of normality, helping to determine if any deviations from the normal distribution are statistically significant. By providing insights into how well the sample data fits the normal distribution, these tests play a crucial role in statistical analysis and the validity of many statistical methods that assume normality.

The other types of tests mentioned in the choices are not specifically focused on assessing normality. Standard deviation tests relate more to measuring variability within a dataset, while correlation tests evaluate the relationship between two variables rather than assessing the distribution’s shape. Chi-square tests are used for categorical data and not suited for assessing the normality of continuous data distributions. Thus, the emphasis on goodness-of-fit tests is what makes option A the correct choice in this context.