Let me try one more time, and make it super concrete. Suppose that we repeatedly do the following 4 measurements:
- We produce a correlated pair. Alice measures the spin of her particle along axis a. Bob measures along axis b
- We produce a correlated pair. Alice measures along a, Bob measures along b'
- We produce a correlated pair. Alice measures a'. Bob measures b
- We produce a correlated pair. Alice measures a'. Bob measures b'
We do these four things over and over, N times. (So we actually produce 4N correlated pairs)
Then we compute:
C(a,b) = \frac{1}{N} \sum_n a_n b_n (where n ranges over 1, 5, 9, etc.)
C(a,b') = \frac{1}{N} \sum_n a_n b_n' (where n ranges over 2, 6, 10, etc.)
C(a',b) = \frac{1}{N} \sum_n a_n' b_n (where n ranges over 3, 7, 11, etc.)
C(a', b') = \frac{1}{N} \sum_n a_n' b_n' (where n ranges over 4, 8, 12, etc.)
Now, the hidden-variable assumption is this: Although
- nobody measured a_n b_n when n=2, 3, 4, 6, 7, 8, 10, 11, 12...
- nobody measured a_n b_n' when n=1, 3, 4, 5, 7, 8, 9, 11, 12...
- nobody measured a_n' b_n when n=1, 2, 4, 5, 6, 8, 9, 10, 12...
- nobody measured a_n' b_n' when n=1, 2, 3, 5, 6, 7, 9, 10, 11...
Those variables had definite values. So even though we don't know what the values were for some variables on some rounds, it makes sense to talk about the following averages:
- D(a,b) = \frac{1}{4N} \sum_n a_n b_n
- D(a,b') = \frac{1}{4N} \sum_n a_n b_n'
- D(a',b) = \frac{1}{4N} \sum_n a_n' b_n
- D(a',b') = \frac{1}{4N} \sum_n a_n' b_n'
where this time, all sums extend over all values of n from 1 to 4N.
The assumption is that
- D(a,b) \approx C(a,b)
- D(a, b') \approx C(a, b')
- D(a', b) \approx C(a', b)
- D(a', b') \approx C(a', b')
That is, the assumption is that the unmeasured quantities have the same statistical properties as the measured quantities. In the limit as N \rightarrow \infty, it is assumed that the averages C approach the averages D.