Final answer:
The distribution of values taken by a statistic in all possible samples from the same population is known as the sampling distribution of the statistic, capturing the sampling variability of the statistic and often approximating a normal distribution for large sample sizes.
Step-by-step explanation:
The distribution of values taken by a statistic in all possible samples of the same size from the same population is called the sampling distribution of the statistic. When we draw simple random samples of size n from a population and measure a characteristic such as the mean, proportion, or standard deviation for each sample, the probability distribution of all these measured characteristics forms the sampling distribution. Essentially, a statistic is a numerical characteristic of a sample that estimates a population parameter. Variability in these statistics from sample to sample is governed by this sampling distribution, and one of the key attributes of this distribution is the standard error, which measures the sampling variability of a statistic. The larger the sample size (n), the more the sampling distribution of the mean approaches a normal distribution, according to the Central Limit Theorem.