The general rule for the use of the Central Limit Theorem to reasonably assume that is normally distributed
When the distribution of is unknown or known to be skewed is for sample sizes greater than or equal to 30.
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True
ONLY If false, change the underlined portion by putting the correct answer here (leave blank if true):

True! The Central Limit Theorem states that as the sample size increases, the sampling distribution of the sample mean will approach a normal distribution, regardless of the shape of the original distribution of . Typically, a sample size of 30 or more is considered adequate for this approximation to hold, particularly when dealing with skewed distributions.

However, if the underlying population distribution is normal, even smaller sample sizes can yield a normally distributed . So, if your population is perfectly normal, you could work with just a few samples and still be golden!