Explanation:
Sampling is a process of selecting a subset of individuals or observations from a population in order to gather information about the whole population. When a sample is taken from the population, it is important to determine how precisely the sample represents the population. For this purpose, a measure of precision obtained by sampling is given by the standard error of the sample mean.

Standard Error:
The standard error of the sample mean is a measure of the variability of the sample mean. It is the standard deviation of the sampling distribution of the sample means. The sampling distribution is the distribution of all possible sample means that could be obtained from the population. The standard error is a measure of how much the sample mean is likely to vary from the population mean.

Sampling Fluctuation:
Sampling fluctuation is the variation in the sample mean due to random chance. It is the difference between the sample mean and the population mean that is due to sampling error. Sampling fluctuation is a source of error in sampling and can be reduced by increasing the sample size.

Sampling Distribution:
The sampling distribution is the distribution of all possible sample means that could be obtained from the population. The sampling distribution is important because it helps to determine how likely it is that the sample mean is representative of the population mean. The shape of the sampling distribution is affected by the sample size and the variability of the population.

Expectation:
Expectation is a measure of the central tendency of a distribution. It is the average value of a random variable over an infinite number of trials. Expectation is an important concept in statistics because it helps to determine the characteristics of the population from a sample.

Therefore, the correct answer to the given question is option 'A', which is the standard error of the sample mean.