The sampling distribution of the mean of a sample of 20 members of a population with a mean of 35 and a standard deviation of 2.8 can be described as approximately normal, with a mean of 35 and a standard deviation of approximately 1.4 (calculated as 2.8 / sqrt(20)).
This is because the central limit theorem states that the distribution of the mean of a sample will be approximately normal, even if the underlying population distribution is not normal, as long as the sample size is large enough. In this case, a sample size of 20 is large enough to justify the use of the central limit theorem.
It's important to note that this description of the sampling distribution assumes that the sample is drawn randomly and independently from the population. If the sample is not drawn randomly or if there are dependencies between the samples, the sampling distribution may not be normal and may require additional analysis to describe accurately.