The ANOVA or F test determines if significant differences exist among treatment means or interactions. It’s a preliminary test that indicates if further analysis of the relationship among treatment means is warranted. If the null hypothesis of no difference among treatments is accepted, the test result implies factor levels and response are unrelated, so the analysis is terminated. When the null hypothesis is rejected, the analysis is usually continued to examine the nature of the factor-level effects. Examples are:
- Tukey’s Method — tests all possible pairwise differences of means
- Scheffe’s Method — tests all possible contrasts at the same time
- Bonferroni’s Method — tests, or puts simultaneous confidence intervals around a pre-selected group of contrasts
The 2-way Unequal Sample Size F-Test is designed to execute on the entire dataset. No group-by parameter is provided for this test, but if such a test is desired, multiple tests must be run on pre-prepared datasets with group-by variables in each as different constants. Two or more treatments must exist in the data within the dataset.
This test creates a temporary work table in the Result Database and drop it at the end of processing, even if the Output option to “Store the tabular output of this analysis in the database” is not selected.
Given a table name of tabulated values, an F-Test is produced. The N-Way ANOVA tests whether a set of sample means are all equal (the null hypothesis). Output is a p-value which when compared to the user’s threshold, determines whether the null hypothesis should be rejected.