Here's an example of how this is going to be applied. Let say we have a random set of points, image 1. Now we want to get an idea for where in general the greatest probability is of finding the next data point. This is what the Probability Density shows in image 2. Image 2 also has the data points superimposed on the density field. Image 3 is just the density field by itself. It gives a general idea of where approximately to expect the next data point. It's not necessary to know exactly where a data point is going to fall. This is also more consistent with Quantum Lottery Selection. In addition, this will be important in proving whether or not a lottery is being tampered with (a.k.a. Fixed) to produce number combinations that deliberately avoid personally picked numbers. In a way, this will act like a form of instrumentation for examining Random Numbers or Quantum Lottery Selection.

The blue areas indicate a low probability of finding or determining a data point, and the increasing gradient to red indicate a high probability of finding or determining a data point.

Image 1	Image 2	Image 3