Contents:1. Introduction;
2. 'Doing science': hypotheses, experiments and disproof;
3. Collecting and displaying data;
4. Introductory concepts of experimental design;
5. Doing science responsibly and ethically;
6. Probability helps you make a decision about your results;
7. Working from samples: data, populations and statistics;
8. Normal distributions: tests for comparing the means of one and two samples;
9. Type 1 and type 2 error, power and sample size;
10. Single factor analysis of variance;
11. Multiple comparisons after ANOVA;
12. Two-factor analysis of variance;
13. Important assumptions of analysis of variance: transformations and a test for equality of variances;
14. Two-factor analysis of variance without replication, and nested analysis of variance;
15. Relationships between variables: linear correlation and linear regression;
16. Linear regression;
17. Non-parametric statistics;
18. Non-parametric tests for nominal scale data;
19. Non-parametric tests for ratio, interval or ordinal scale data;
20. Introductory concepts of multivariate analysis;
21. Introductory concepts of sequence analysis;
22. Introductory concepts of spatial analysis;
23. Choosing a test; Appendix A. Critical values of chi-square
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