CLT means Central Limit Theorem.
It says that if you take many random samples from a population and calculate the mean of each sample, the distribution of those sample means will become approximately normal as the sample size gets larger, even if the original population is not normal.
In simple CFA terms:
Population distribution: The original data may be skewed or messy.
Sample mean: The average from one sample.
Sampling distribution of the mean: The pattern you get if you take many samples and calculate many sample means.
CLT idea: As sample size increases, those sample means tend to form a normal-shaped distribution.
Why it matters: It allows analysts to use normal-distribution tools for inference, confidence intervals, and hypothesis testing.
CLT does not mean the original data becomes normal. It means the distribution of sample means becomes approximately normal.
No comments:
Post a Comment