I did. A sample of integers in which half of them are divisible by 3, then "refuted" by a new sample in which only 1/3 are. Although this is an "extreme" case it is not a special case, there will always be a change strictly greater than zero if the sample size is strictly smaller than the population size.
The positive case can be argued if you accept that a larger sample is better (more evidence) than a smaller sample, then taking a second sample enlarges your total sample and hence increases your certainty of the hypothesis derived from the first sample.
I think you are missing the thrust of the argument.
You seem to be equating a sample which accurately reflects the characteristics of the population as being equivalent to a "good" hypothesis - after all, that's where you are getting your validation.
However, the hypothesis is constructed based on the sample, specifically to match it. The hypothesis in such circumstances
must match the population to the extent the sample does. So long as my sample does match the population, any further sampling with the same fidelity will also match the population. We haven't done anything new.
"I looked in my driveway and my car was there. I hypothesize that pixies put it there. To support this hypothesis, I looked again to see that not only was the car there, but so were the tires, the engine, and the windshield."
"Well, OK. I believe you. The car is there. Any further sampling you do will give us the same result. But what about those pixies?"
"The pixies put the car there. The car being there supports my hypothesis."
"But you only made that up after you knew the car was there!"
"Yes, but I re-sampled and the car was still there."
"But you didn't add any new information."
"Oh, but my wife looked from the other side and saw the car too. She also spotted the little door where you put gas in."
"Did she see any pixies?"
"No. The pixies put the car there, they don't stick around."