GLM: Statistical Hypothesis Testing

This lecture by the instructor covers the efficiency of a design in neural signals and signal processing, focusing on the concept of partial correlation and the application of the General Linear Model (GLM) to voxel timecourses. It delves into statistical hypothesis testing within GLM, explaining the derivation of contrasts and the assessment of evidence for specific hypotheses. The lecture also explores error types in GLM, including false positives and false negatives, and discusses the importance of thresholding and multiple comparisons in statistical analysis.