CHAPTER 7 1. True or false questions. 1. True or False: As the sample size increases, the standard error of the mean increases. 2. True or False: If the population distribution is symmetric, the sampling distribution of the mean can be approximated by the normal distribution if the samples contain 15 observations. 3. True or False: If the population distribution is unknown, in most cases the sampling distribution of the mean can be approximated by the normal distribution if the samples contain at least 30 observations. 4. True or False: As the sample size increases, the effect of an extreme value on the sample mean becomes smaller. 5. True or False: The Central Limit Theorem ensures that the sampling distribution of the sample mean approaches normal as the sample size increases 6. True or False: The standard error of the mean is also known as the standard deviation of the sampling distribution of the sample mean. 7. True or False: A sampling distribution is defined as the probability distribution of possible sample sizes that can be observed from a given population. 8. True or False: As the size of the sample is increased, the standard deviation of the sampling distribution of the sample mean for a normally distributed population will stay the same. 9. True or False: The fact that the sample means are less variable than the population data can be observed from the standard error of the mean. II. Multiple choice questions. 10. Sampling distributions describe the distribution of a) parameters. by statistics. c) both parameters and statistics. d) neither parameters nor statistics. 11. The standard error of the mean a) is never larger than the standard deviation of the population. b) decreases as the sample size increases. c) measures the variability of the mean from sample to sample. d) All of the above. 12. The Central Limit Theorem is important in statistics because a) for a large n, it says the population is approximately