Final answer:
The correct answer is A. Central Limit Theorem, which states that the sampling distribution of the sample mean approximates a normal distribution as the sample size becomes large, typically n ≥ 30.
Step-by-step explanation:
The answer to the student's question is A. Central Limit Theorem. The Central Limit Theorem states that regardless of the shape of the population, the sampling distribution of the sample mean becomes approximately normal for n, the sample size, sufficiently large. This theorem is pivotal because it allows for the normal approximation in situations where the population distribution is unknown or non-normal.
As n increases, the standard error of the mean decreases, which signifies that the sample mean is likely to be closer to the population mean μ. Using this theorem, particularly when n ≥ 30, allows for more precise estimations about populations based on sample data.