Statistics & Statistical Analysis Illustrated: Variance Everywhere by Kottemann Jeffrey

Author:Kottemann, Jeffrey
Language: eng
Format: epub, pdf
Published: 2022-02-23T00:00:00+00:00

Figure 3.8

Note that this analysis doesn’t tell you which group is different from which group, just that statistical difference(s) exist between some group(s) and other(s). Upon rejecting the Null Hypothesis, all the various possible pairings of groups (D vs. R, D vs. I, and R vs. I) would need to be analyzed for sample mean differences using t-tests, as shown in the previous section.[x]

Now suppose the survey was administered in both Flowing Wells and Artesian Wells. Now we could perform two-way ANOVA. Table 3.1 shows the general structure of the sample mean statistics to be analyzed. ANOVA analysis will analyze differences across communities (FW vs AW) as well as across political affiliations (D vs R vs I). Those are called main effects. It will also analyze what are called interaction effects. For example, if the sample means were all equal to 2 except for Artesian Wells Independents equal to 5, then there would be an interaction effect, meaning the interaction of living in Artesian Wells and being an Independent seems special. A more common example is where the interaction between two drugs may have special beneficial or deleterious effects that neither drug alone has and even more than the sum of benefits or deleterious effects the two have individually.

Table 3.1

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