Using the Central Limit Theorem, it is found that the statement is true, as for sample sizes of 30 or more, the sampling distribution of sample means is normal.
It states that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].
For a skewed variable, the sampling distribution is also approximately normal, as long as n is at least 30.
Hence, the statement is true, as for sample sizes of 30 or more, the sampling distribution of sample means is normal, no matter the underlying distribution of the variable.
More can be learned about the Central Limit Theorem at https://brainly.com/question/24663213
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