Answer:
Explanation:
According to the central limit theorem, a distribution is said to be normal if the sample size is sufficiently large (usually n > 30) notwithstanding whether the source population is normal or skewed. But for a population that is normally distributed the theorem still holds true. for samples smaller than 30.
The shape of the sampling distribution changes with a sample size. For a large sample size, the sampling distribution starts to approximate a normal distribution.