By the end of this course, students will understand how to:

1) design hypothesis-informed studies that directly relate to 
planned analyses
2) implement appropriate analytical approaches
3) deal effectively with real-world heterogeneity in field studies
4) manage and visualize data responsibly

1. Introduction to experimental design and statistical inference

• Review of probability theory, distributions, and descriptive 
statistics

• Data visualization: responsible data exploration and examining 
distributions

2. Designing successful field studies: philosophies and  
relationships to statistical inference

• Experimental vs. observational study design

• Dealing with heterogeneity in the real world: appropriate 
controls, replication, and pseudoreplication

• Visualizing experimental designs and hypotheses

• Responsible and effective data management strategies

3. General linear models (2 weeks)

• Regressions, ANOVA, ANCOVA

• Dealing with heterogeneity in the real world: data 
transformations 

• Correcting for multiple comparisons

• Data visualization: discrete vs. continuous variables

4. Generalized linear models (2 weeks)

• Binomial and Poisson families

• Data visualization: back-transforming from link functions

5. Mixed models: fixed and random effects (2 weeks)

• Dealing with heterogeneity in the real world: blocking, 
nesting, stratifying

• Temporal vs. Spatial variation

• Data visualization: how to deal with random effects

6. Multivariate analyses of community data (2 weeks)

• Measuring distances among groups in multivariate space

• Ordination techniques to reduce dimensionality: PCA, PCoA, CA, 
NMDS

• Inference tools: PERMANOVA, ANOSIM, PERMDISP

• Data visualization: visualizing multi-dimensional points and 
axes

7. Intro to structural equation models

• Pros and cons of SEMs; difference vs. multiple regression

• How to interpret a SEM figure

8. Intro to model selection and multi-model inference

• Statistical philosophies: Information-theoretic approach

• Responsible model selection: a priori hypotheses

9. Intro to Bayesian probability and inference

• Statistical philosophies: Bayes theorem

• Markov Chain Monte Carlo techniques

• How to interpret results from Bayesian analyses

UGA Student Honor Code: "I will be academically honest in all of 
my academic work and will not tolerate academic dishonesty of 
others." A Culture of Honesty, the University's policy and 
procedures for handling cases of suspected dishonesty, can be 
found at www.uga.edu/ovpi. Every course syllabus should include 
the instructor's expectations related to academic integrity.