Copy and Edit

BIOL2022 L16 INFORMATION-THEORETIC METHODS vs NULL HYPOTHESIS TESTING

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
Michael Jardine
Quiz by Michael Jardine, updated more than 1 year ago
Michael Jardine
Created by Michael Jardine over 5 years ago
10
0
0

Resource summary

Question 1

Question
Which of the following are alternatives to null-hypothesis testing?
Answer
  • 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].
Answer
  • 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?
Answer
  • 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 (binary/octet-stream)
Answer
  • Model 1
  • Model 2
  • Model 3
  • Insufficient information

Question 5

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

Question 6

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

Question 7

Question
Which of the following is the Akaike Weight?
Image:
L16 Image 2 (binary/octet-stream)
Answer
  • 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
Show full summary Hide full summary

Want to create your own Quizzes for free with GoConqr? Learn more.

Similar

BIOL2022 L01 Why statistics? Organisms are variable in a variable world.
Michael Jardine
BIOL2022 L02 Considerations of sampling. The need to have statements that are testable; operating in a logical structure
Michael Jardine
BIOL2022 L03 Sampling and experiments: Design is all! #1 BEING REPRESENTATIVE when sampling biological systems
Michael Jardine
BIOL2022 L05 Testing differences between two means: 1- and 2-tailed t-tests
Michael Jardine
BIOL2022 L07 Three cardinal sins: Non-independence, Confounding, Pseudoreplication
Michael Jardine
BIOL2022 L19 Multiple response variables
Michael Jardine
BIOL2022 L21 Cluster analysis
Michael Jardine
BIOL2022 L22 nMDS and hypothesis testing
Michael Jardine
BIOL2022 L23 PERMANOVA, DISCRIMINANT ANALYSIS, MANOVA
Michael Jardine
BIOL2022 L24 Rarefaction and Diversity
Michael Jardine
BIOL2022 L14 DETECTING DIFFERENCES vs DESCRIBING RELATIONSHIPS
Michael Jardine
Browse Library