The is a minimum-variance unbiased point estimate of the mean of a normally distributed population

Sample mean is the answer

Step-by-step explanation:

Recall the central limit theorem.

Suppose that a sample is obtained containing a large number of observations, each observation being randomly generated in a way that does not depend on the values of the other observations, and that the arithmetic mean of the observed values is computed. If this procedure is performed many times, the central limit theorem says that the distribution of the average will be closely approximated by a normal distribution. A simple example of this is that if one flips a coin many times the probability of getting a given number of heads in a series of flips will approach a normal curve, with mean equal to half the total number of flips in each series. (In the limit of an infinite number of flips, it will equal a normal curve.)

Hence we find that sample means is a minimum-variance unbiased point estimate of the mean of a normally distributed population

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step-by-step explanation: