Central Limit Theorem: The central limit theorem states that regardless of the shape of a population distribution, as long as sample sizes are large enough, sample means will follow an approximately normal distribution.
Confidence Interval: A confidence interval provides a range within which we can be reasonably confident that the true population parameter lies. It takes into account both sample statistics and variability.
Sampling Distribution: A sampling distribution represents all possible samples that could be drawn from a population and shows how their statistics (e.g., means) vary. It helps us make inferences about populations based on samples.