Permutation Testing
The main idea behind the permutation test as it is implemented in BESA Statistics is that if a statistical effect is found over an extended time period in several neighboring channels, it is unlikely that this effect occurred by chance. Thus, the initial step is to define data clusters that show a significant effect between groups / conditions (see Preliminary Statistics). Then it is tested if the initial data clusters survive permutation. Permutation means that the data of subjects get systematically interchanged. Based on the permutation results, the significance of the initial cluster can be determined.
Significant data clusters are visualized and their p-value is displayed.
Results of the permutation test are corrected for multiple comparisons.
Permutation Test Results for Image Data
Significant data clusters are displayed and their p-values are given