Most statistical t ests begin by identifying a null hypothesis. The null hypothesis for t he pattern
analysis tools (Analyzing Patterns toolset and Mapping Clusters toolset) is Complet e Spatial
Randomness (CSR), either of the features themselves or of the values associated wit h those
features. The z-sc ores and p-values returned by the pattern analysis tools tell you whet her
you can rejec t that null hypothesis or not. Often, you will run one of the pat tern analysis
tools, hoping that the z-score and p-value will indic ate t hat you can reject t he null
hypothesis, because it would indicate that rather than a random patt ern, your features (or the
values associated with your features) exhibit st atist ically signific ant c lustering or dispersion.
Whenever you see spatial structure, like clustering, in the landscape (or in your spatial dat a),
you are seeing evidence of some underlying spatial processes at work, and as a geographer or
GIS analyst, this is often what you are most int erest ed in.