Final answer:
A sampling distribution refers to the distribution of sample means (B) from all possible random samples of a certain size from a population, approaching a normal distribution as sample size increases.
Step-by-step explanation:
A sampling distribution is the probability distribution of a given statistic based on a random sample. When discussing the distribution of sample means, we are referring to the collection of sample means from all possible random samples of a certain size (n) that can be drawn from a population. The question at hand is asking specifically about what a sampling distribution refers to, and the correct answer is B) Sample means. As the sample size increases, the sampling distribution of the mean approaches a normal distribution, assuming all samples are randomly selected and independent, and the population standard deviations are equal. This concept is crucial for statistical inference, allowing us to make predictions about population parameters based on sample statistics.
One important characteristic of the sampling distribution is that the standard deviation of this distribution, referred to as the standard error of the mean, can be calculated as the population standard deviation divided by the square root of the sample size (s/√n). This measure helps in estimating how much the sample mean will vary from the population mean.