

Course ID:  ECOL 4800/68004800L/6800L. 4 hours. 6 hours lecture and 8 hours lab per week. 
Course Title:  Statistical Thinking in Ecology 
Course Description:  Statistical concepts and principles for both experimental and
observational investigation in the ecological sciences.
Students will explore the fundamental and advanced issues in
ecological/environmental measurement processes that will aid
scientific discovery. Students will learn hypothesis driven
search skills and weight of evidence portrayals of research
findings. 
Oasis Title:  STAT THINK ECOL 
Nontraditional Format:  This course will be offered as a Maymester at the UGA Savannah
River Ecology Laboratory (SREL) Conference Center, near Aiken,
SC. Class size will be restricted to 1516 students. 
Prerequisite:  (STAT 2000 and ECOL(BIOL) 35003500L) or permission of department 
Semester Course Offered:  Not offered on a regular basis. 
Grading System:  AF (Traditional) 

Course Objectives:  Students will learn to give statistical thinking to their
ecological research interests before collecting one single data
point. They will learn the philosophy of the Hypothetico
deductive loop for scientific inquiry, how to craft sampling
plans, and common experimental design flaws or missing
features. They will learn to distinguish between measurement
and sampling uncertainty, the essential concept of variance
partitioning, and the estimation of sampling variability via
randomization methods (e.g., bootstrap sampling) and
simulation. Finally, they will learn the value of portraying
the weight of evidence in ecological research via data display
that may or may not require a formal statistical inference. 
Topical Outline:  Lecture 1. Statistical Thinking
A. It’s the science of science
1. Peeling the artichoke (on falsification)
2. Eureka! (On verification  HD loops)
3. What about corroboration?
B. What scientists really want to know
1. The frequentist in most of us
2. The Bayesian in all of us
Lecture 2. The Essential Beginning
A. Formulating the right problem
1. What you think is the problem vs. what is the problem
2. It’s a matter of perspective, and a function of experience
3. The role of consulting  and not just with statisticians
B. Scope of inference
1. What you can say vs. what you can't say
2. It’s in how you collect the data
Lecture 3. Distinguishing Multiple Sources of Variation
A. Random variation: measurement processes
B. Systematic variation: natural factors
C. Bias
Lecture 4. Identifying the Experimental Unit
A. Measurement scale
B. Scale transformation
Lecture 5. Purposeful Investigation
A. Discovery oriented
1. It’s more about natural history
2. Taxonomically descriptive
3. What we find over here
B. Hypothesis driven
1. It’s more about ecology
2. Contextually interpretive
3. Pattern recognition
Lecture 6. Sampling Plans
A. Addressing specific questions
B. Regarding accuracy vs. precision
C. The more variable, the more samples
D. Avoiding confounding
Lecture 7. Weight of Evidence
A. Summary data display vs. tabular presentation
B. Role of significance testing
1. Pvalue reliance
2. The erroneous concept of asymptotic certainty
Lecture 8. The Statistical Model
A. Explaining relationships
B. Predicting additional measurements
Lecture 9. Drowning in Measures
A. Np dilemma: N = sample size, p = no. response variables
measured
B. Dimensionality reduction
1. Multivariate methods may or may not apply
2. Indices may be more helpful, but less statistically
rigorous
3. The tradeoff 