A researcher takes 10 samples of 20 students each to get a sampling distribution of the average number of siblings students at a university have. According to the Central Limit Theorem, what can the researcher do make their sampling distribution get closer to normal?

• A. Increase the number of samples
• B. Increase the sample size
• C. Decrease sample size
• D. Decrease number of samples

Answer: (B)

Verified step by step guidance

1. Understand the Central Limit Theorem (CLT): The CLT states that the sampling distribution of the sample mean will approach a normal distribution as the sample size increases, regardless of the population's distribution.
2. Identify the key factors affecting the normality of the sampling distribution: These include the sample size (number of individuals in each sample) and the number of samples taken.
3. Analyze the options provided: Increasing the sample size (number of individuals in each sample) will reduce variability and make the sampling distribution closer to normal. Increasing the number of samples improves the estimate of the sampling distribution but does not directly affect its normality.
4. Focus on the correct approach: To make the sampling distribution closer to normal, the researcher should increase the sample size (number of students in each sample). This is because larger sample sizes reduce the impact of outliers and skewness in the population distribution.
5. Conclude: The correct answer is 'Increase the sample size,' as this aligns with the principles of the Central Limit Theorem and directly impacts the normality of the sampling distribution.