Two clocks problem and measurements 
If I have one clock, I know what the time is.
If I have two clocks, I now am unsure.

Although simplified, the problem is related to a more complex system I am testing.
If I have the following measurements from two systems:
Attribute   System 1   System 2
A           0          0
B           10         100
C           90         100
D           100        110
E           900        1000

then I'd like a way to determine some value of error between the systems (assuming I cannot distinguish which system, if any, is correct)
So

*

*attribute A values are considered no-error or both systems correct

*attribute B values are considered a larger error than A, C, D and E values

*attributes C, D and E values are have the same error (or similar error)

Also, assume the attributes are independent.
My understanding of statistics is low, and I can only recall some things about relative error, but iirc I require knowing whether one system is correct to determine the relative error of the other system.
How does one go about resolving this issue?
 A: Sorry to disappoint you, but I don't see any definitive statistical solution to your problem.  There's no glorious trick that can determine where truth lies and which of your systems is closer to it.  So what can you do?  Obtain multiple measurements from each system and average them...  Average the measurements you have and treat those averages as best estimates...  Devise additional systems, making your problem potentially better and potentially worse.
A: A few things that might help (a bit).  


*

*You could calculate the coefficient of variation for each attribute.  This would allow you to at least quantify what you are saying when you say (for example) "the error in Attribute B is higher than the error of the other attributes".

*You could use analysis of variance to quantify how much of the variation is between attributes, and how much is between your two measuring systems.

*You could model the system2 measurements on the system1 measurements and hence get a quantification of the relationship between the two (crudely speaking, it would come up with something like "system 2 on average returns 10% higher estimates than system 1").
None of these completely fix your problem but they give you some different angles to look at it.
