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 [itex]a[/itex]. Bob measures along axis [itex]b[/itex]
- We produce a correlated pair. Alice measures along [itex]a[/itex], Bob measures along [itex]b'[/itex]
- We produce a correlated pair. Alice measures [itex]a'[/itex]. Bob measures [itex]b[/itex]
- We produce a correlated pair. Alice measures [itex]a'[/itex]. Bob measures [itex]b'[/itex]
We do these four things over and over, N times. (So we actually produce 4N correlated pairs)
Then we compute:
[itex]C(a,b) = \frac{1}{N} \sum_n a_n b_n[/itex] (where [itex]n[/itex] ranges over 1, 5, 9, etc.)
[itex]C(a,b') = \frac{1}{N} \sum_n a_n b_n'[/itex] (where [itex]n[/itex] ranges over 2, 6, 10, etc.)
[itex]C(a',b) = \frac{1}{N} \sum_n a_n' b_n[/itex] (where [itex]n[/itex] ranges over 3, 7, 11, etc.)
[itex]C(a', b') = \frac{1}{N} \sum_n a_n' b_n'[/itex] (where [itex]n[/itex] ranges over 4, 8, 12, etc.)
Now, the hidden-variable assumption is this: Although
- nobody measured [itex]a_n b_n[/itex] when [itex]n=2, 3, 4, 6, 7, 8, 10, 11, 12...[/itex]
- nobody measured [itex]a_n b_n'[/itex] when [itex]n=1, 3, 4, 5, 7, 8, 9, 11, 12...[/itex]
- nobody measured [itex]a_n' b_n[/itex] when [itex]n=1, 2, 4, 5, 6, 8, 9, 10, 12...[/itex]
- nobody measured [itex]a_n' b_n'[/itex] when [itex]n=1, 2, 3, 5, 6, 7, 9, 10, 11...[/itex]
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:
- [itex]D(a,b) = \frac{1}{4N} \sum_n a_n b_n[/itex]
- [itex]D(a,b') = \frac{1}{4N} \sum_n a_n b_n'[/itex]
- [itex]D(a',b) = \frac{1}{4N} \sum_n a_n' b_n[/itex]
- [itex]D(a',b') = \frac{1}{4N} \sum_n a_n' b_n'[/itex]
where this time, all sums extend over all values of [itex]n[/itex] from [itex]1[/itex] to [itex]4N[/itex].
The assumption is that
- [itex]D(a,b) \approx C(a,b)[/itex]
- [itex]D(a, b') \approx C(a, b')[/itex]
- [itex]D(a', b) \approx C(a', b)[/itex]
- [itex]D(a', b') \approx C(a', b')[/itex]
That is, the assumption is that the unmeasured quantities have the same statistical properties as the measured quantities. In the limit as [itex]N \rightarrow \infty[/itex], it is assumed that the averages [itex]C[/itex] approach the averages [itex]D[/itex].