Why do we estimate population parameters using statistic? I had been studying statistics, I have a doubt that I couldn't find the answer of. Its related to estimating population parameters using statistic.
Suppose we have a population size of 10000, we want to estimate mean for it since it is too costly to collect data for all the observations. As far as I know standard process is to arrive on a sample size, say, 100 find the mean for this sample, iterate these steps over some time and then arrive at population mean and its standard deviation assuming normal distribution.
Now, doing this we are not estimating population mean but we are estimating the mean of samples for which we have the observation data. So, if we have the observations for 100 data points, we are estimating mean of this sample of 100 rather than the population of 10000. Then why do we call it estimating population mean?
 A: "As far as I know standard process is to arrive on a sample size, say, 100 find the mean for this sample, iterate these steps over some time and then arrive at population mean and its standard deviation assuming normal distribution."
I think this is your fundamental misunderstanding. It is standard to take a single sample and use statistics calculated from the sample to estimate the required parameters.
Your point of confusion seems to be how we estimate the uncertainty in our parameter estimates. We do so by considering the "sampling distribution" of the estimator - imagining how it would be distributed if we took hundreds of samples, but not actually doing so. Looking at how widely spread out the sampling distribution is gives us a measure of uncertainty for our estimate - the standard error of the sampling distribution is called the "standard error". Larger samples have a smaller standard error, so less uncertainty. If we could afford to take multiple samples, we are actually better off to take one large sample! 
In the special but common case where the parameter of interest is the mean, we estimate it using the sample mean (usually), and if the original data is normally distributed then the sampling distribution of the sample mean is too. Even if the sample is not drawn from a normal distribution, for large samples we can appeal to the Central Limit Theorem and say the sample mean is approximately normally distributed. The meaning of a "large" sample depends just how far from normality the original population is; if it is symmetric and unimodal a fairly small sample may suffice, but a very skewed population may require a fairly substantial sample for the sample mean to be reasonably close to normally distributed.
A: See, the very word estimation implies that you are not necessarily getting the exact value, but reasonably close enough. 
In sampling theory, the sample mean is an unbiased estimate  of the population mean. Therefore, if you have a set of randomly selected 100 data points, you can be pretty much sure that the mean of this sample will not be far off from the mean of the population. If the number of samples are increased, the accuracy goes up, but never touches 100% in general. You should refer to the error of estimation. But increasing the number of samples beyond a point is not efficient, and the whole point of using samples to determine the population properties is lost. Therefore, you decide an acceptable error bar, and then find the required sample size. 
A: We estimate population parameters because we don't have exactly values or completed data  in any population example when we want to estimate the mean and the standard deviation of 10000 sample size became difficult as why
