What is the Central Limit Theorem and how does it relate to point and interval estimation?
- It implies that every data set is symmetrically distributed, which affects the reliability of point and interval estimations
- It suggests that all data has a central tendency and this affects the point and interval estimations
- It suggests that as sample size increases, the distribution of sample means approaches a normal distribution, which affects how we estimate population parameters
- It suggests that every large enough dataset is normally distributed, which is the foundation of point and interval estimations
The Central Limit Theorem states that when you have a sufficiently large sample, the distribution of the sample mean approximates a normal distribution, regardless of the shape of the population distribution. This allows us to make inferences about the population parameters using the sample mean and the standard error, which form the basis of point and interval estimation.
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