I'm just at the beginning of learning this stuff. I learnt that we can obtain what is called the 'expected mean' of the sample, which is basically the mean that we'd expect to get if we sampled the sample many times.
In the specific case below, we have one sample of an overall population. The book then assumes that this specific case's mean and standard deviation is exactly the same as actual mean of the population. My understanding is that the specific case's mean and standard deviation of a given sample are not necessarily the same as those of the statistic X bar, which is what we usually use as the unbiased estimator of the mean of the population. Even when we look at another unbiased estimator of the mean of the original population, which is E(X), we still end up with the fact that this doesn't necessarily mean that the expected mean of the original population is the same as the mean of the specific case of a given sample
So, WHY is the mean of a specific case sample being used as an unbiased estimator of the mean of the entire population? Someone please help clear my misconceptions :)