A sample distribution refers to a probability distribution that describes the possible values and their corresponding probabilities for statistics calculated from multiple samples drawn from the same population. It shows how different samples might produce different statistics.
Related terms
Central Limit Theorem: This theorem states that as long as certain conditions are met, regardless of what population distribution looks like, when taking sufficiently large random samples, the sampling distribution will approximate normality.
Sampling Error: Sampling error refers to discrepancies between sample statistics and actual population parameters due to random chance inherent in selecting only part of the population for study.
Confidence Interval: A confidence interval is an estimate of a population parameter, accompanied by a margin of error. It provides a range within which the true value of the parameter is likely to fall.