Final answer:
A sample statistic is a numerical summary of a sample, such as the average of 1,000 grocery visits for produce spending, and a sampling distribution is the probability distribution of a statistic across many samples, typically approximated by a normal distribution for large samples.
Step-by-step explanation:
A sample statistic is a numerical characteristic that summarizes or describes an aspect of a sample. For example, the average expenditure on produce per visit by the sample of 1,000 shopping visits is a statistic that estimates the parameter, which is the average expenditure on produce per visit by all the store's customers (the population).
The sampling distribution is the probability distribution of a given statistic based on a random sample. It describes the variability of the statistic from sample to sample. For example, consider the mean expenditure on produce per visit across many different samples of shopping visits. If we model this with a probability distribution, that would be a sampling distribution, often approximated by a normal distribution due to the Central Limit Theorem when the sample size is sufficiently large.