Lecture
This lecture delves into the General Linear Model (GLM) method, focusing on hypothesis testing, t-tests, F-tests, multiple comparisons, and enriching the model to account for imaging artifacts and physiological noise. It covers the Gauss-Markov condition, type-I and type-II errors, sensitivity, specificity, and the F-test. The lecture also explores the use of F-contrasts, Gaussian random field theory, and the fallacy of hypothesis testing. Additionally, it discusses parameter estimation, group-level analysis, ANOVA, and alternative statistical testing methods like permutation testing. The instructor emphasizes the importance of assessing effect size and offers teaser questions to reinforce understanding.