POS 3713 - POS 3713: Understanding Political Science Research: Unit Exam 3. Questions and Answers.

POS 3713 - POS 3713: Understanding Political Science Research: Unit Exam 3. Questions and Answers. Understanding Political Science Research (POS 3713‐02, Fall 2017, HCB 113) Prof. Jens Großer Unit exam #3 11/09/2017 This is the third out of four multiple choice tests (unit exams) that count for your final grade. There are 25 questions with four possible answers each. In all questions, only one of the four answers is correct. You will receive 1 point for each correct answer, and 0 points for each incorrect answer. Hence, the maximum possible points in this test are 25 points. Please read the questions and possible answers carefully before you make your choices! Also, don’t forget to write down and “bubble” your name on the answer sheet! On Chapter 6: 1. What is the key insight of the Central Limit Theorem? (a) Regardless of the shape of a frequency distribution, as the sample size gets larger and larger, that distribution will more closely resemble a normal distribution, and, when a sample hypothetically becomes infinitely large, it is guaranteed to be normally distributed. (b) For variables that are normally distributed, it is possible to use even a small sample to make inferences about the population. (c) Regardless of the shape of a frequency distribution of a randomly chosen sample, a hypothetical distribution of an infinite number of sample means will be normally distributed, with a knowable variance. (d) For random samples, but only for random samples, the process of statistical inference does not depend on sample size. 2. Which of the following is not true about normal distributions? (a) It is the basis of statistical inference. (b) Approximately 68% of the area under the curve lies within one standard deviation of its mean. (c) It is symmetrical about its mean. (d) As the name implies, they are quite common in everyday experience. 3. Which of the following is not true about a sampling distribution? (a) A sampling distribution represents a hypothetical distribution, showing what the true population distribution would look like if our sample size were infinitely large. (b) A sampling distribution for a variable is normally distributed even if the underlying population is not distributed normally for that variable. (c) Sampling distributions are almost never observed in real life. (d) The mean of a sampling distribution is equal to the true population mean.