But that's the same population I used to construct my model to match. In my view, matching is not enough. To count as a useful model, it should have explanatory power as well. And, by explanatory power, I mean it suggests different connections and outcomes from those we used to construct the model in the first place.
I'll try one more example to see if we can agree.
Suppose I sample (using good random technique) the natural numbers. I take a sufficiently large sample (say a few million) and find that about half are evenly divisible by two. I now suggest that the entire population of natural numbers (if infinity bothers us here, we can set some very high limit instead) has this property.
I sample again and get the same results (within some acceptable margin of error).
Now, you seem to be saying the second sampling confirms my hypothesis in some way. I'm saying that it adds no new information, since one of the characteristics of my first sampling was that it represents the entire population fairly. The best I might say is I have demonstrated good sampling technique, not that my hypothesis is anything more than a restatement of the results of my sampling.
If that's misleading or wrong, perhaps you could give an example which illustrates your point - because I suspect we are talking at cross purposes here.