Suppose $\{A_k\}_{k=1}^\infty$ and $\{B_k\}_{k=1}^\infty$ are measurable sets in the same probabiilty space such that $P(A_k)=P(B_k)$ for all $k$. If you can show that $P(A_k \text{ i.o.})=1$ (for example, the $A_k$'s are independent and $\sum_{k=1}^\infty P(A_k)=\infty$) then is it necessarily true that $P(B_k \text{ i.o.})=1$? (Obviously if I used Borel-Cantelli for the $A_k$'s, I am assuming that the $B_k$'s are not necessarily independent.) I don't think this is so, but I can't come up with a counterexample yet.
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The big point is this: we have no information about how the $A_k$ correlate with each other or how the $B_k$ correlate with each other. So, we can build a scenario like this:
Suppose we have a fair coin, which we flip over and over again.
For $k\in\mathbb{N}$, let $A_k$ be the event that the $k$th flip yields heads; let $B_k$ be the event that the first flip yields heads.
Then $P(A_k)=P(B_k)=\frac{1}{2}$ for all $k$, and $P(A_k\text{ i.o.})=1$, but $$ P(B_k\text{ i.o})=P(\text{first flip gives heads})=\frac{1}{2}. $$
Nick Peterson
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For the rest: slightly more general is the Kolmogorov 0-1 law; it says that if you have a sequence of random variables $(X_n){n=1}^{\infty}$ and a _tail event for the sequence - that is, an event that is independent of any finite initial sequence of the random variables - then that event must have probability 0 or 1. To get more general is difficult - it really depends on the nature of the relationship between your variables.
– Nick Peterson Jul 26 '13 at 02:59