Why don’t we calculate the average of an entire given population instead of computing confidence interval to estimate the population mean? We compute confidence intervals to estimate the true population mean of either a sample (when population standard deviation is unknown) or an entire population (when population standard deviation is known). But I wonder why, if we are given an entire population, why don't we calculate the average of that population instead of computing confidence interval to estimate the population mean since we already have the entire population? I'm not a Math or Stat major so I apologize if my question does not sound "smart".

We’d love to calculate population parameters!
All of inferential statistics is about inferring. In other words, we are using our data at hand to guess about something greater than the data (e.g., the population from which the data are drawn). We can be silly with our guesses, or we can be thoughtful. Good statisticians intend to be thoughtful in order to make good guesses.
Those guesses are the inferences.
If we had the whole population, we wouldn’t have to guess, so inference would not be useful. We would just calculate the population parameters, and that’s the end. Alas, we tend to be interested in something greater than our data, so inferences are necessary.
The specific example you give of doing a z-test with a known variance and unknown standard deviation is a special case. With real data, we never know the true variance. However, such a test is useful as a first example of how to do hypothesis testing, and it serves a useful educational purpose in a “Stat 101”-type of class.
