How do you know if your population distribution is too skewed to use the Central Limit Theorem?
Is there a way to quantify that? Like, a skewness of x degrees is too skewed? Or does the CLT always work whenever the sampling distribution approaches the normal distribution? (Of course, while complying with the requirement of a sample above 30)
Hm. I’m slightly confused by the question.
Central Limit Theorum refers to the sampling distribution (of the mean) is a normal distribution (bell curve) if the sample size is large enough…regardless of whether or not the actual population is a normal distribution.
If the population distribution is skewed, a larger sample size is needed.