The real problem is most samples are not random. So, you are bound by the bias of your methods and you can't really get all that accurate. In theory when you double your sample size you do reduce your margin of error by a reasonable degree, but reality does not mesh until you start taking a large percentage of the population.
Think of it like a coin, that has a 1% bias you want the percentage to some accuracy (say 4 digits) how many flips do you need?. Now what if the problem is not the coin but the person doing the flipping. At some point more testers help more than more flips.
Think of it like a coin, that has a 1% bias you want the percentage to some accuracy (say 4 digits) how many flips do you need?. Now what if the problem is not the coin but the person doing the flipping. At some point more testers help more than more flips.