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BIOL2022 L16 INFORMATION-THEORETIC METHODS vs NULL HYPOTHESIS TESTING

Descripción

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
Test por Michael Jardine, actualizado hace más de 1 año
Michael Jardine
Creado por Michael Jardine hace más de 5 años
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Resumen del Recurso

Pregunta 1

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

Pregunta 2

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

Pregunta 3

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

Pregunta 4

Pregunta
AIC determines that which model is “best”?
Image:
L16 Image 1 (binary/octet-stream)
Respuesta
  • Model 1
  • Model 2
  • Model 3
  • Insufficient information

Pregunta 5

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

Pregunta 6

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

Pregunta 7

Pregunta
Which of the following is the Akaike Weight?
Image:
L16 Image 2 (binary/octet-stream)
Respuesta
  • 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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