I have $n$ bins, $n$ red balls, and $n$ green balls. I throw the balls into randomly selected bins one at a time. Once a bin has $2$ balls in it, I close the bin. It matters to me how many bins will have two red balls at the end.
Does the order I throw the balls matter?
The answer is yes, which I've verified through simulation and looking at the $n=2$ case, but I'm wondering whether there is a powerful conceptual explanation. This could take the form of calculating the expected number of red/red bins for any $n$ in ordered vs randomized sets of balls and noting the difference in the process, or it could take some other form.
The $n=2$ case is in itself a perfectly cromulent argument, of course, but we can also see there is a difference for large $n$ by looking at the probability of getting no red/red bins at all.
If we're throwing all the red balls first, then each of them needs to land in a bin that hasn't been hit yet, so $$ P_{\text{reds first}}(\text{no red/red}) = \frac{n}{n}\frac{n-1}{n}\cdots\frac{1}{n} = \frac{n!}{n^n} $$
On the other hand, if we're throwing balls in a random order, then we might as well start by throwing $2n$ numbered balls in order (closing bins along the way) and only after we know which number goes where decide which of the numbers are red and green. This means that we end up with the same distribution as if we start by picking two balls for the first bin, then two balls for the next bin, and so forth. To avoid having any red/red bins the second ball we choose for each bin must be of the opposite color from the first, so $$ P_{\text{random order}}(\text{no red/red}) = \frac{n}{2n-1}\frac{n-1}{2n-3}\frac{n-2}{2n-5}\cdots\frac{1}{1} = \frac{n!}{(2n)!/(2^n n!)} = \frac{2^n(n!)^2}{(2n)!} $$
Using Stirling's approximation, the logarithms of these go as (ignoring terms of order $O(\log n)$): $$ \log\frac{n!}{n^n} \approx n \log n - n - n\log n = -n $$ $$ \log \frac{2^n(n!)^2}{(2n)!} \approx n\log 2 + 2n\log n - 2n - 2n \log 2n + 2n = -n\log 2 $$ Since $\log2 < 1$, the probability for no red/red in the "reds first" model decreases strictly faster than in the "random order" model.
This doesn't mean that the expected number of red/red bins for the two models can't have the same behavior, but at least if you're after the actual probability distribution on the number of red/red bins, then the order matters, also asymptotically.