Final answer:
The probability distribution of all the measured characteristics from simple random samples is called a sampling distribution. The sampling distribution of the sample means approaches a normal distribution as the sample size increases. The answer to the student's question is D. sampling distribution.
Step-by-step explanation:
The probability distribution for all possible values of a sample statistic is known as a sampling distribution. When dealing with simple random samples of size n from a population, and measuring a characteristic (e.g., mean, proportion, or standard deviation) for each sample, the collection of these measured characteristics forms the sampling distribution.
If you draw random samples of size n, the distribution of the random variable X, consisting of sample means, is called the sampling distribution of the mean. As the sample size n increases, the sampling distribution of the mean approaches a normal distribution.
The normal distribution is a continuous probability distribution characterized by a bell-shaped curve, where the total area under the curve equals one. It is defined by two parameters: the mean (μ) and the standard deviation (σ). A special case is the standard normal distribution, which is a normal distribution with a mean of zero and a standard deviation of one, noted as Z ~ N(0, 1).
Contrasting to normal distribution, the uniform distribution is also a continuous probability distribution but concerned with events that are equally likely to occur and has different applications. Therefore, the answer to the initial question is D. sampling distribution.