T-test and ANOVA

T-tests are designed for smaller samples.

Three ways to compare means of two groups (sample vs. population, sample vs. sample, pre vs. post)

pg 308-320 is not on the test. Scan it, but don't do z-test practice problems.

pg 339 #3 question (make sure you understand how to answer this.)

Data enter one column for age and one column for status (married or bachelor).



ANOVA

Chapter 12

T-test only accomodates comparing 2 means. ANOVA is used to compare more than 2 means. Multiple t-tests inflates the alpha level, so we use ANOVA, since a extreme sample would show up multiple times in the pairings. Sample 1 vs. Sample 2, Sample 1 vs. Sample 3, etc. If sample 1 is an outlier, each pairing might give you a type 1 error.

ANOVA is the workhorse of statistics...any research question you have, many would conform it to how to run an ANOVA. That is a little backwards, but it has been like that in the past.

We will study between-groups and repeated-measure ANOVAs.

ANOVA (analysis of variance)

When to use ANOVA:
1. More than two means compared
2. The group means should vary widely from grand mean (mean of means)
3. The groups' raw data does not vary widely. (Close clustering of scores)

Total variance = Variance + error or Between-groups variance + within-groups variance

Between-groups variance is the good stuff. (treatment + error)
Within-groups variance is the bad stuff (variance due to error only since each member of the group is exposed to the same treatment).

F statistic = BG/WG or (treatment + error)/error

F = 1 (means treatment had no effect)