Explanation:

In statistics, when we collect data from a population, it is often not feasible or practical to collect data from the entire population. Instead, we take a sample from the population and use that sample to make inferences about the population.

When we take multiple samples from the same population and calculate a statistic for each sample, we get a set of values known as the sampling distribution of that statistic. The sampling distribution is the distribution of all possible values of the statistic that could be obtained from different samples of the same size from the same population.

Importance of Sampling Distribution:

The sampling distribution is important because it allows us to make inferences about the population based on the sample. By understanding the properties of the sampling distribution, we can determine the accuracy and reliability of our estimates.

Example:

Let's consider an example to illustrate the concept. Suppose we want to estimate the mean height of all students in a school. It is not practical to measure the height of every student, so we take a random sample of 100 students and calculate the mean height of that sample. If we repeat this process multiple times, we will get a set of mean heights for each sample.

Sampling Distribution of the Mean:

In this example, the statistic of interest is the mean. Each sample will have a different mean height, and if we plot the distribution of all these means, we will get the sampling distribution of the mean. The shape of the sampling distribution will depend on the shape of the population distribution, but it is known to follow a normal distribution under certain conditions (such as the Central Limit Theorem).

Conclusion:

In summary, when we take multiple samples from a population and calculate a statistic for each sample, we obtain the sampling distribution of that statistic. The sampling distribution is a theoretical distribution that allows us to make inferences about the population based on the sample. In the given question, the distribution being referred to is the sampling distribution, and the statistic being referred to is the statistic calculated from each sample. Therefore, the correct answer is option 'A' - the distribution of a statistic is called the sampling distribution of that statistic.