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.

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. P-value 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. N-p 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 trade-off