Instructor: Brian Gerber
Classroom: Engineering E106
When: Mo/We 3 - 4:15pm
My Office: 202A Wagar, Cooperative Research Unit
Office hours: We 1pm - 2pm and by appointment
brian.gerber@colostate.edu
Primarily:
Main Objective:
A mix of…
Some Important Concepts and Terminology
Inference
Sampling distribution
Sampling variation
Sampling bias
Sample size
Estimator bias and variance
Name / Department
What does ‘Sampling’ mean to you?
Motivation for taking this course?
Experience with the R programming language
What is your ideal job, research project, study animal, or study location?
Assessment Components | Percentage of Grade |
---|---|
Course Engagement | 10% |
Assignments | 25% |
Quizzes | 25% |
Test | 20% |
Project | 20% |
An oral presentation on a topic of choice that focuses on the ideas of sampling
Thinking from a sampling perspective is a super power!
Fundamental to scientific learning
Efficient learning about the world
Don’t need any damn statistical model!
Interpret and frame learning from any “study”
Learning is a choice (in every moment)
Words Matter
An inclusive environment is paramount for learning
Communication is everything
Everyone has something to teach and something to learn
Struggle is good. Solving problems leads to learning. But….
https://bgerber123.github.io/FW552/
Brook trout habitat in rivers of Massachusetts
A finite part of a target population whose properties are studied to gain information about the whole (i.e., to infer or gain inference).
Brook trout habitat in rivers of Massachusetts
A selection of rivers (sample) to measure habitat characteristics (e.g. gravel beds) to be used to infer about habitat in all of the state.
Piping plovers nesting at Watch Hill, RI
Census: a complete enumeration of a target population
The process of selecting a representative part of the population for the purpose of determining characteristics of the target population.
Why do we need to sample?
Impossible to know whether a sample is ‘good’ (i.e., representative of the target)
The sample itself cannot tell us - only the process
Given exact same sample, we treat it differently depending on how it was selected.
A sample from a reliable process has credentials