In each experiment, we assign users randomly based on a hash of experiment name and userid. So, we assume (and have tested) that the groups of one experiment have a fairly uniform mix of users from the groups of any other experiment. This means the primary effect of being in a particular experiment group G should be much larger than the effect of being in G and some other experiment's group. So, we usually analyze the effects of an experiment independent of any other experiments. Millions of users helps here.
I think this is how trials normally work: assume that random assignment into groups will spread all other factors equally across the groups.
There are experiments which depend on each other (change the same UI, one assumes a precondition of another), and that situation is complicated. We try to avoid it when we can, and negotiate carefully when we find ourselves in it.
You might also be referring to the fact that looking at hundreds of things will find "statistically significant" effects simply at random. As my advisor said, "95% confidence means wrong 1 in 20 times." That is a risk to be managed. We always have to ask things like: is this a reasonable outcome for this experiment? Do we have other corroborating evidence? Is the effect consistent? Do we have reason to disbelieve the result? Some fraction of our experiments are run incorrectly or have a broken implementation; we detect some, likely not all.
Also: is the risk higher to run an experiment that may be misinterpreted, or not to experiment at all and just release things? We like to learn from data if we think we can. There is art in the tradeoffs.