1. Introduction: 1.1. Statistics and the life sciences; 1.2. Types of evidence; 1.3. Random sampling -- 2. Description of Samples and Populations: 2.1. Introduction; 2.2. Frequency distributions; 2.3. Descriptive statistics: measures of center; 2.4. Boxplots; 2.5. Relationships between variables; 2.6. Measures of dispersion; 2.7. Effect of transformation of variables (optional); 2.8. Statistical inference; 2.9. Perspective -- 3. Probability and the Binomial Distribution: 3.1. Probability and the life sciences; 3.2. Introduction to probability; 3.3. Probability rules (optional); 3.4. Density curves; 3.5. Random variables; 3.6. The binomial distribution; 3.7. Fitting a binomial distribution to data (optional) -- 4. The Normal Distribution: 4.1. 4.2. The normal curves; 4.3. Areas under a normal curve; 4.4. Assessing normality; 4.5. 5. Samling Distributions: 5.1. Basic ideas; 5.2. The sample mean; 5.3. Illustration of the Central Limit Theorem (optional); 5.4. The normal approximation to the binomial distribution (optional); 5.5. .6. Confidence Intervals: 6.1. Statistical estimation; 6.2. Standard error of the mean; 6.3. Confidence interval for μ 6.4. Planning a study to estimate μ 6.5. Conditions for validity of estimation methods; 6.6. Comparing two means; 6.7. Confidence interval for (μ1 [ -- ] μ2); 6.8. Perspective and summary -- 7. Comparision of Two Independent Samples: 7.1. Hypothesis testing: the randomization test; 7.2. Hypothesis testing: the t test; 7.3. Further discussion of the t test; 7.4. Association and causation; 7.5. One-tailed t tests; 7.6. More on interpretation of statistical significance; 7.7. Planning for Adequate power (optional); 7.8. Student's t: conditions and summary; 7.9. More on principles of testing hypotheses; 7.10. The Wilcoxon-Mann-Whitney test; 7.11. 8. Comparison of Paired Samples: 8.1. 8.2. The paired-sample t test and confidence interval; 8.3. The paired design; 8.4. The sign test; 8.5. The Wilcoxon Signed-Rank test; 8.6. 9. Categorical Data: One-Sample Distributions: 9.1. Dichotomous observations; 9.2. Confidence interval for a population proportion; 9.3. Other confidence levels (optional); 9.4. Inference for proportions: the Chi-Square goodness-of-fit test; 9.5. 10. Categorical Data: Relationships: 10.1. 10.2. The Chi-Square Test for the 2 X 2 contingency table; 10.3. Independence and association in the 2 X 2 contingency table; 10.4. Fisher's exact test (optional); 10.5. The r X k contingency table; 10.6. Applicability of methods; 10.7. Confidence interval for difference between probabilities; 10.8. Paired data and 2 X 2 tables (optional); 10.9. Relative risk and the odds ratio (optional); 10.10. Summary of Chi-Square test -- 11. Comparing the Means of Many Independent Samples: 11.1. 11.2. The basic one-way analysis of variance; 11.3. The analysis of variance model; 11.4. The global F test; 11.5. 11.6. One-way randomized blocks design; 11.7. Two-way ANOVA; 11.8. Linear combinations of means (optional); 11.9. Multiple comparisons (optional); 11.10. 12. Linear Regression and Correlations: 12.1. 12.2. The correlation coefficient; 12.3. The fitted regression line; 12.4. Parametric interpretation of regression: the linear model; 12.5. Statistical inference concerning β1; 12.6. Guidelines for interpreting regression and correlation; 12.7. Precision in prediction (optional); 12.8. Perspective; 12.9. Summary of formulas -- 13. A Summary of Inference Methods: 13.1. 13.2. Data analysis examples