The sampling distribution is a common source of misuse and misunderstanding in the study of statistics. The sampling distribution, underlying distribution, and the Central Limit Theorem are all interconnected in defining and explaining the proper use of the sampling distribution of various statistics. The sampling distribution of a statistic is used to find probabilities of research outcomes and is one of the key concepts in statistical significance testing. Sampling distributions are the frequency distributions of a particular sample's statistics and contain infinitely many statistics for a given sample size from a population. The most common sampling distribution is the sampling distribution of the mean. The mean of this distribution is assumed to be the true mean of the population. There are generalizations, qualities, and rules that have to be observed in order for the sampling distribution to produce parameter estimates that can be used to make experimental inferences. The exact use of the sampling distribution in significance testing, the future of significance testing in the study of behavior, and alternatives to employing statistical significance testing in the traditional sense are also explored. (Contains 28 references.) (Author/SLD)