Preview of ANOVA
Definition of Analysis of Variance
- Analysis of Variance (ANOVA) is a hypothesis
testing procedure that is used to evaluate differences
between the means of two or more treatments or
groups (populations). ANOVA uses sample data to
make inferences about populations.
Goals of ANOVA
- Conceptually, the goal of ANOVA is to determine the
amount of variability in groups of data, and to see if
the variability is greater between groups than within
ANOVA & T-Tests:
- ANOVA is a more general version of the t-test in two
- Both tests use sample data to test hypotheses about
population means. ANOVA, however, can test hypotheses
about two or more population means. The T-Test
can only test hypotheses about two population means.
- T-Test can only be used with one independent (classification)
variable, whereas ANOVA can be used with more any
number of independent (classification) variables.
Like the T-Test, ANOVA can be used with either independent
or dependent measures designs. That is, the several
measures can come from several different samples (independent
measures design), or they can come from repeated measures
taken on the same sample of subjects (repeated --- dependent
--- measures design).
- Chapter 13 covers only the very simplest type of ANOVA:
One-Way Independent Measures ANOVA (called "single-factor
Independent Measures" designs in the book).
- Chapter 14 covers one-way (single-factor) repeated
measures designs. We don't cover this.
- Chapter 15 covers two-way independent measures designs.
I'll talk about this briefly.