A ECL 371

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mdonovan
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172932
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A ECL 371
Updated:
2012-09-24 00:35:57
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ECL 371
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A ECL 371 Exam 1
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  1. Laws of Association VS. Experiments (Chapter 2)
    • Association (Descriptive)
    • -Most Wildlife studies 
    • -Watching, Mointering

    • Experiments
    • -Test Hypothesis
    • -Controlled Setting
  2. Induction (Chapter 2)
    Make generalization based on an individual observation 

    Ex: This Drake Mallard has a green head, so all drake Mallards have green heads
  3. Deduction (Chapter 2)
    Move from general premises to specific conclusions

    Ex: All vertebrates breath oxygen. A bluegill is a vertebrate, so bluegill breathe oxygen.
  4. Things to Consider when doing a Study. (Chapter 2)
    1. Design and Desired Inference

    2. Sample Size and Power

    3. Data Collection 

    4. Data Analysis Methods 

    5. Evaluation and Interpretation 

    6. Speculation/ New Hypothesis
  5. Six Main Sampling Designs (Chapter 2)
    1. Simple Random 

    2. Systematic 

    3. Stratified Random (by habitat)

    4. Cluster 

    5. Adaptive 

    6. Sequential
  6. What is Bias? What is Precision? What are the 4 Types of Combinations for Bias and Percision (Chapter 2)
    • -Bias - on or off the target
    • -Precision - points grouped or ungrouped 

    1. Unbiased and Precise = Accurate

    2. Unbiased but Not Precise = Not Accurate 

    3. Biased and Precise = Not Accurate 

    4. Biased and Not Precise = Not Accurate
  7. Statistics Hypothesis Test (Chapter 2)
    -Type I vs. Type II Errors
    Null Hypothesis True-Reject Null Hypothesis = Type I Error

    Null Hypothesis True-Fail to Reject Hypothesis = Correct

    Null Hypothesis False-Reject Null Hypothesis = Correct 

    Null Hypothesis False-Fail to Reject Hypothesis = Type II Error
  8. The 5 different Study Designs (Chapter 2)
    1. Field Studies 

    2. Natural Experiments 

    3. Field Experiments 

    4. Laboratory Experiments

    • 5. Integrated Research Process
    •       -Combination of above
  9. What are the Alternatives to Experimental Testing? (Chapter 2)
    • - Regression and Linear Models 
    • - Model Building 
    • - Bayesian Statistics

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