The correct answer is: B. Population is not normally distributed, but sample size is large.
The sampling distribution of the mean is the probability distribution of the sample mean, calculated from a sample of size $n$ from a population with mean $\mu$ and standard deviation $\sigma$. The sampling distribution of the mean is approximately normally distributed when the sample size is large, regardless of the shape of the population distribution.
This is because the central limit theorem states that, as the sample size increases, the sampling distribution of the mean will approach a normal distribution, regardless of the shape of the population distribution. The central limit theorem is a powerful tool that allows us to make inferences about a population based on a sample, even when the population distribution is unknown.
In practice, a sample size of 30 or more is often considered large enough for the sampling distribution of the mean to be approximately normally distributed. However, the exact sample size required for the sampling distribution to be approximately normal will depend on the shape of the population distribution. For example, if the population distribution is very skewed, a larger sample size may be required for the sampling distribution of the mean to be approximately normal.