1. Displaying the order in a group of numbers.
2. Central tendency and variability.
3. Some key ingredients for inferential statistics: Z scores, the normal curve, sample versus population, and probability.
4. Introduction to hypothesis testing.
5. Hypothesis testing with means of samples.
6. Making sense of statistical significance: Effect size and statistical power.
7. Introduction to the t test: Single sample and dependent means.
8. The t test for independent means.
9. Introduction to the analysis of variance.
10. Factorial analysis of variance.
13. Chi-square tests.
14. Strategies when population distributions are not normal: Data transformations and rank-order tests.
15. Integration and the general linear model.
16. Making sense of advanced statistical procedures in research articles.