[A quick review of SAMPLING: When one system relates to another across a boundary, they are said to be in relationship. They interact by co-sampling information that comes to them across their boundaries from the other system. Each system has a way of detecting the other system, and the information that transfers between systems can be clear or degraded depending on the type of boundary (perspective/POV/experimental setup/pathway) across which one system detects, or samples, the other. Think of sampling as one object or system reaching out to grab some information about the other. Data returned depends not just on the character of the boundary, but the speed and duration of the sample or sampling.] PERSPECTIVE OR POINT OF VIEW (POV) depends on a change in the data that we sample. A change in the data from one data point to another depends on the probability that a data point is sampled in the here and now. One example of the above statement is to look at two perspectives. Let’s say we’re watching a train moving down a track. In space, or along a distance, we know that the change between the first data point sampled and the second data point sampled must necessarily describe a distance or path or straight line in normal space of nominal curvature. The only way we can see this path is if we view the train perpendicular to the tracks. Taking another perspective, we watch the train head-on(along the tracks). Now we cannot see its path from this perspective, but the two data points we sample return a different sort of information which must be mapped onto the information derived from the first perspective (in this way information from the first perspective can be understood and compared with respect to the second perspective). The second perspective, watching the train head on, returns the centroid of the front of the train. The outline of the train will expand, if coming toward us, or contract, if moving away. Notice, now we can characterize the samples more easily with respect to probability. If our location is fixed, as the train approaches us, its signal increases in intensity, which is what it should do if its probability of being sampled (at our location) increases. And as the train recedes, its signal decreases in intensity, which is what it should do if its probability of being sampled decreases. Because we sampled differently on both occasions (watching changes in space perpendicular to the movement of the train and then watching changes in time as we watch the train head-on) we need some sort of translation between the two sets of data. Taking the two data points from the two different perspectives does not represent a controlled experiment. A controlled experiment requires a single perspective. In more complex experimental sampling, perspectives are born from diverse experimental setups. The data obtained from a scanning-tunneling microscope varies greatly from that derived from a light scope. There might be a way to compare the information derived from the two perspectives, but we’d never say that independent of mapping one perspective on another, we could in any way call data from the two perspectives a controlled experiment. Now let’s look at the perspective perpendicular to the track. If we can sample at the starting point of the train and the ending point of the train, at each of those two points the train will be there or it won’t be there. If the train is at that precise location that we sample instantaneously, it is approximately 100% probable there. If it isn’t there, it’s near 0% probable. Notice that sampling the two points from each POV returns information about the probability of the existence of our relationship with that particular sample. In other words, the chance of finding the sample at a given location can be, depending on perspective, anywhere from 0% to 100%. This information will be important when we consider our interpretation of the results of the Double-Slit Experiment. It also has a profound influence on our understanding the difference between a sample’s tendency to be local as opposed to its tendency to be nonlocal. To understand the percentage chance of collecting a sample from any POV, we might delve into the definition of probability in time (chance) as opposed to the probability in space (distribution) with weather as an example.