Sampling distributions refer to the probability distributions that describe statistics calculated from samples taken from populations. They help us make inferences about population parameters based on sample statistics.
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Sample Mean: The sample mean is the average value calculated from observations within a sample.
Central Limit Theorem: As mentioned earlier, the central limit theorem plays an important role in sampling distributions by stating that regardless of the shape of the population distribution, as long as your sample size is large enough, your sampling distribution will be approximately normally distributed.
Confidence Interval: A confidence interval provides an estimated range of values which likely contains an unknown population parameter. It is calculated from the data in a sample.