![]() Because the computation of the F statistic is slightly more involved than computing the paired or independent samples t test statistics, it's extremely common for all of the F statistic components to be depicted in a table like the following: For an independent variable with k groups, the F statistic evaluates whether the group means are significantly different. The test statistic for a One-Way ANOVA is denoted as F. Balanced designs (i.e., same number of subjects in each group) are ideal extremely unbalanced designs increase the possibility that violating any of the requirements/assumptions will threaten the validity of the ANOVA F test.Each group should have at least 6 subjects (ideally more inferences for the population will be more tenuous with too few subjects).Researchers often follow several rules of thumb for one-way ANOVA: Note: When the normality, homogeneity of variances, or outliers assumptions for One-Way ANOVA are not met, you may want to run the nonparametric Kruskal-Wallis test instead. When variances are unequal, post hoc tests that do not assume equal variances should be used (e.g., Dunnett’s C). ![]() When this assumption is violated, regardless of whether the group sample sizes are fairly equal, the results may not be trustworthy for post hoc tests. ![]() These conditions warrant using alternative statistics that do not assume equal variances among populations, such as the Browne-Forsythe or Welch statistics (available via Options in the One-Way ANOVA dialog box).
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