Thanks for help, but i still cant get the needed answer. Let me explain the problem more clearly. I have a data set which looks like that (-1, 0.8, -1, -1, 0.9,0, 1.1, 0, -1,...........,0.7, 0, 0.9), N=1000, mean ~0.04. I am using bootstrapping technique to find out the distribution and i get X~N(0.05, 0.02). Everything is clear for me here. If i put mean, std dev, and lower/upper bounds of mean to proc power to get N i get ~300. Thats look totally normal as i was guessing that 300 would be enough before trying to calculate this. But if i choose 300 random observations from data set containing 1000 observations and calculate mean, every time it is not even close to 0.05. Even running bootstrapping when generating 300 samples means where N =300 i get totally different results. So how do i get N with which mean would be similar in both cases with all data from set and with N=300 ? Problem: for example i want to collect another data set with similar observations (it takes too long to get another 1000 observations) and i want to decide when number of observations is enough to conclude that mean is equal to some number and it will be the same in a long term so and i can invest real money into these observations.
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