# BIOL2022 L16 INFORMATION-THEORETIC METHODS vs NULL HYPOTHESIS TESTING

Quiz by Michael Jardine, updated more than 1 year ago
 Created by Michael Jardine almost 2 years ago
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### Description

Module 2, Lecture 5 No Learning Outcomes, but Summary: • Bayesian and Information Theory approaches: alternatives to null-hypothesis testing • AIC determines the model of best fit with the number of parameters • AIC is NOT a measurement of support for a model • Model averaging recognises uncertainty in models by calculating average parameters weighted by Akaike weights • Note: CANNOT undo bad sampling or poor experimental design

## Resource summary

### Question 1

Question
Which of the following are alternatives to null-hypothesis testing?
• Information Theory
• Bayesian approaches
• Two-factor ANOVA

### Question 2

Question
AIC stands for [blank_start]________[blank_end] [blank_start]________[blank_end] [blank_start]________[blank_end].
• Akaike
• Akake
• Alternative
• Arbitrary
• Information
• Informatic
• Informatics
• I can't think of a 4th option here
• Criterion
• Criteria
• Critique
• Computation
• Critiques
• Computations

### Question 3

Question
Which of the following are considered part of Information Theory?
• Simplicity
• Parsimony
• Multiple working hypotheses
• ‘Strength of Evidence’
• Null hypothesis testing
• P-hacking

### Question 4

Question
AIC determines that which model is “best”?
Image:
L16 Image 1
• Model 1
• Model 2
• Model 3
• Insufficient information

### Question 5

Question
True or false: AIC tells you which model is best.
• True
• False

### Question 6

Question
True or false: AIC tells you how good each model is.
• True
• False

### Question 7

Question
Which of the following is the Akaike Weight?
Image:
L16 Image 2
• Relative likelihood of the model
• Difference between the model AIC and the minimum AIC
• AIC adjusted for finite sample size
• AIC adjusted for infinite sample size
• None of the above

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