Biology 300 Course Outline

1. Introduction: Cats, statistics, and other stories.
   
   
2. Data description: Kinds of data, frequency tables, histograms, cumulative frequency distributions, mean, median, mode, range, standard deviation, variance.
   
   
3. Introduction to estimation: Samples and populations, population and sample histograms, distribution of sample means, standard error of the sample mean.
   
   
4. Introduction to hypothesis testing: Null vs alternate, statistical significance, goodness of fit tests, the chi-square distribution, statistical errors, log-likelihood ratio, Kolmogorov-Smirnov test.
   
   
5. Probability: Basic rules, mutually exclusive events, independence, probability trees.
   
   
6. Contingency tables: Tests of independence of two nominal variables, chi-square tests, 2 × 2 tables, Fisher's exact test, log-likelihood ratio.
   
   
7. The Binomial and Poisson distributions: Probabilities, binomial test, goodness of fit to binomial and Poisson distributions.
   
   
8. The Normal distribution: Probabilities under the normal curve, normal approximation to the binomial distribution.
   
   
9. One-sample inference (normal case): Distribution of sample means, confidence intervals for the sample mean, one- and two-tailed hypotheses, the t-distribution, distribution of sample variance, testing departures from normality.
   
   
10. One-sample inference (non-normal case): Central Limit Theorem, normal approximation, nonparametric alternatives.
    
    
11. Two-sample inference: Confidence limits and tests for the difference between two variances, and two means, normal approximation, nonparametric alternatives (Mann-Whitney U-test).
    
    
12. Paired samples: Paired t-test, confidence limits for mean difference, nonparametric alternatives (Wilcoxon paired-sample test).
    
    
13. Notes on tests of significance: Significance vs importance, one vs two-tailed tests, testing many hypotheses.
    
    
14. Experimental design: Treatments and controls, experimental vs observational studies, beware the sample of convenience.
    
    
15. Introduction to multisample inference: The analysis of variance (ANOVA) for comparison of several treatment means, one-way ANOVA, random and fixed effects, homogeneity of variances, data transformations, nonparametric alternatives (Kruskall-Wallis test).
    
    
16. Multiple comparisons: The a posteriori comparison of means, Tukey test, Newman-Keuls test, confidence intervals.
    
    
17. Data transformations: Log, arcsine square root, and square root.
    
    
18. Linear regression: Bivariate data, scatterplots, dependent and independent variables, estimation and tests for slope and intercept, predicting values of Y, data transformation, comparing two slopes.
    
    
19. Correlation: Linear correlation coefficient, confidence intervals and tests, comparing correlation coefficients, nonparametric alternatives (rank correlation).
    
    
20. Introduction to experiments with more than one factor