To apply statistical inference, it is important to understand the concept of what is known as a sampling distribution. A sampling distribution is the set of all possible values of a statistic, along with their probabilities, assuming that we sample at random from a population where the null hypothesis holds true.

A more simplistic definition is this: a sampling distribution is the set of values that the statistic can assume (distribution) if we were to repeatedly draw samples from the population, along with their associated probabilities.

The value of a statistic is a random sample from the statistic's sampling distribution. The sampling distribution of the mean is calculated by obtaining many samples of various sizes and taking their mean. 

The central limit theorem states that the sampling distribution is normally distributed if the original or raw-score population is normally distributed, or if the sample size is large enough. Conventionally, statisticians define large enough sample sizes as N ≥ 30—that is, a sample size of 30 or more. This is still a topic of debate, though.

The standard deviation of the sampling distribution is often referred to as the standard error of the mean, or just the standard error.