- -ANOVA and MANOVA (Statsoft.com)
Describes methods by topic.
- ANOVA - Introduction (University of Leicester)
Describes the conditions that must be met in order to use the ANOVA and more.
- F-Test (University of Duesseldorf)
"We can easily do power analyses for single-factor and multi-factor experiments. In G*Power, you select F-Test (ANOVA), Global for ANOVA, fixed effects: Single-factor designs, or F-Test (ANOVA), Special for ANOVA, fixed effects: Multi-factor designs, ANOVA, fixed effects: Planned comparisons, and Analyses of covariance (ANCOVA)."
- F-Test (Wikipedia.org)
"An F-test is any statistical test in which the test statistic has an F-distribution if the null hypothesis is true. A great variety of hypotheses in applied statistics are tested by F-tests. Among these are given below:"
"The hypothesis that the means of multiple normally distributed populations, all having the same standard deviation, are equal. This is perhaps the most well-known of hypotheses tested by means of an F-test, and the simplest problem in the analysis of variance."
"The hypothesis that the standard deviations of two normally distributed populations are equal, and thus that they are of comparable origin."
- F-Test for Equality of Two Standard Deviations (NIST.gov)
"An F-test ( Snedecor and Cochran, 1983) is used to test if the standard deviations of two populations are equal. This test can be a two-tailed test or a one-tailed test. The two-tailed version tests against the alternative that the standard deviations are not equal. The one-tailed version only tests in one direction, that is the standard deviation from the first population is either greater than or less than (but not both) the second population standard deviation . The choice is determined by the problem."
- Tests for Differences in Variance (University of Leicester)
Describes the conditions that must be met in order to use the F-test and other tests of homogeneity of variance.