Analysis of Variance, ANOVA, is the hypothesis testing procedure used when a study has three or more sample groups
& Coups, 2009).
When using ANOVA, the researcher analyzes the variances. By analyzing these
variances, they are able to conclude what the different means mean. F
represents the F ratio. This is the ratio of the between – groups population
variance estimate to the within-groups population variance estimate (Aron, Aron, & Coups, 2009). In ANOVA testing,
F=1 is what the null hypothesis equals. If the F ratio is larger than 1, then
the null hypothesis is rejected. One of the major differences between a two
sample t test and ANOVA testing is the number of samples study. T test has 2
whereas ANOVA has 3 or more.
Aron, A., Aron, E. N., & Coups, E. J. (2009). Statistics for psychology (5th ed). Upper Saddle River, NJ: Pearson/Prentice Hall.
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