Abstract
This chapter presents the analysis of variance (ANOVA), which is a procedure for testing differences among three or more means for statistical significance. It is also used with just two samples. It permits null hypotheses to be tested that involve the means of three or more samples (groups); however, one-way ANOVA deals with one independent variable. The null hypothesis tested by ANOVA is the means of the populations from which the samples were randomly drawn are all equal; for example, the null hypothesis in the caffeine experiment is HO: μ1= μ2 = μ3 = μ4= μ5. The alternative hypothesis states that HO is not true. The ANOVA procedure is based on a mathematical proof that the sample data can be made to yield two independent estimates of the population variance. The first step in the ANOVA design is to compute the sum of squares between groups (symbolized by SSB), the sum of squares within groups (symbolized by SSW), and the total sum of squares (symbolized by SST). A sum of squares is nothing more than a sum of squared deviations.
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