1. A Repertoire of Hypothesis Tests  z-test – for use with normal distributions and large samples.  t-test – for use with small samples and when the pop std deviation is unknown.  F-test (ANOVA) – for comparing means for multiple groups.  Chi-square test – for use with qualitative data.

2. Null and Alternative Hypotheses  How you write the null and alternative hypothesis varies with the design of the study – so does the type of statistic.  Which table you use to find the critical value depends on the test statistic (t, F, chi-square, U, T, H).  This will be on the final exam.

3. Deciding Which Test to Use  Is data qualitative or quantitative? If qualitative use Chi-square.  How many groups are there? If two, use t-tests, if more use ANOVA  Is the design within or between subjects?  How many independent variables (IVs or factors) are there?

4. Summary of t-tests  Single group t-test for one sample compared to a population mean.  Independent sample t-test – for comparing two groups in a between-subject design.  Paired (matched) sample t-test – for comparing two groups in a within-subject design.

5. Summary of ANOVA Tests  One-way ANOVA – for one IV, independent samples  Repeated Measures ANOVA – for one or more IVs where samples are repeated, matched or paired.  Two-way (factorial) ANOVA – for two or more IVs, independent samples.  Mixed ANOVA – for two or more IVs, between and within subjects.

6. Summary of Nonparametric Tests  Two samples, independent groups – Mann- Whitney (U). Like an independent sample t-test.  Two samples, paired, matched or repeated measures – Wilcoxon (T). Like a paired sample t-test.  Three or more samples, independent groups – Kruskal-Wallis (H). Like a one-way ANOVA.

7. Summary of Qualitative Tests  Chi Square (  2 ) – one variable. Tests whether frequencies are equally distributed across the possible categories.  Two-way Chi Square – two variables. Tests whether there is an interaction (relationship) between the two variables.