Monte Carlo randomization is one way to quantitatively evaluate observed data and test statistics.
In general, Monte Carlo Randomization (MCR) procedures follow this sequence:
Following the calculation of a statistic from the original dataset, observations are randomized.
The statistic is recalculated for the randomized data.
Steps 1-2 are repeated a given number of times, amassing distributions that will be used to calculate P-values for the observed statistic.
P-values are calculated by comparing the observed statistic to the reference distribution.
ClusterSeer randomizes the original dataset according to the approach recommended for a particular method (see Types of spatial randomization or Space-time randomization). Null hypotheses and the randomization approach are detailed in individual method descriptions.