It is impossible to recognize objects that are too close together, and the critical spacing for recognition grows proportionally with eccentricity (Bouma, 1970; Levi, 2008; Pelli & Tillman, 2008).
Specifically, in systems where objects are moving with different velocities, it is impossible to determine which object is "in motion" and which object is "at rest".
However, it is impossible to observe the same object in two states simultaneously.
Eaton-Krauss was able to tell from Carter's excavation journal that the innermost shrine of Tutankhamun's tomb was too small to fit properly around his sarcophagus, suggesting that the sarcophagus had in fact been intended for someone else – something that it is impossible to tell from the objects as they are set up on display in Cairo.
While the programme evaluator observes the factual for an object, it is impossible to observe the counter-factual for the same object.
Since it is impossible to observe for the same object both the result of exposure and non-exposure to an event, causal inference is based on comparing the outcomes on equivalent treatment and control groups, i.e., treatment and control units with similar baseline characteristics.
And it is impossible to believe that any of these objects would have existed in anything like the form we know had Steve Jobs not been around.
However, assuming dynamic backgrounds or target objects, it is impossible to acquire such ground truth without guarantees of the repeatability of the backgrounds and targets.
The notion of 'everyday' thus becomes hopelessly unwieldy, and it is impossible to come up with a list of objects and activities that belong to it.
Before releasing an object-oriented software system, it is impossible to know with certainty when, where, how, and how often a class will be modified.
