Sampling Distribution: A sampling distribution shows all possible values for an estimator (such as sample mean) when repeated samples are taken from a population. The mean value from this distribution corresponds to the population mean.
Parameter Estimation: Parameter estimation involves using sample data to make educated guesses about unknown population parameters. The population mean is often a parameter of interest for estimation.
Central Limit Theorem: The central limit theorem states that, under certain conditions, the sampling distribution of the sample mean approaches a normal distribution regardless of the shape of the population distribution. This theorem is essential for estimating population means.