The standard error of the mean measures the variability or spread of sample means around the population mean. It helps us estimate how accurate our sample mean is as an estimate of the population mean.
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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.