1. A finite population correction factor is needed in computing the standard deviation of the sampling distribution of sample means   whenever the sample size is more than 5% of the population size
2. A numerical measure from a population, such as a population mean, is called   a parameter
3. A numerical measure from a sample, such as a sample mean, is known as  a statistic
4. A population consists of 500 elements. We want to draw a simple random sample of 50 elements from this population. On the first selection, the probability of an element being selected is   0.002
5. A population consists of 8 items. The number of different simple random samples of size 3 (without replacement) that can be selected from this population is   56
6. A probability distribution for all possible values of a sample statistic is known as a   sampling distribution
7. A probability sampling method in which we randomly select one of the first k elements and then select every kth element thereafter is   systematic sampling
8. A sample statistic, such as , that estimates the value of the corresponding population parameter is known as a  point estimator
9. A simple random sample from a process (an infinite population) is a sample selected such that  each element selected comes from the same population and each element is selected independently
10. A simple random sample of 28 observations was taken from a large population. The sample mean equaled 50. Fifty is a   point estimate
11. A simple random sample of size n from a finite population of size N is a sample selected such that each possible sample of size  n has the same probability of being selected
12. A simple random sample of size n from a finite population of size N is to be selected. Each possible sample should have    the same probability of being selected
13. A single numerical value used as an estimate of a population parameter is known as  a point estimate
14. A subset of a population selected to represent the population is a  sample
15. A theorem that allows us to use the normal probability distribution to approximate the sampling distribution of sample means and sample proportions whenever the sample size is large is known as the    central limit theorem
16. As the sample size increases, the   standard error of the mean decreases
17. As the sample size increases, the variability among the sample means   decreases
18. Doubling the size of the sample will   reduce the standard error of the mean to approximately 70% of its current value
19. Excel’s RAND function   generates random numbers
20. For a population with an unknown distribution, the form of the sampling distribution of the sample mean is   approximately normal for large sample sizes
21. How many different samples of size 3 (without replacement) can be taken from a finite population of size 10?  120
22. If we consider the simple random sampling process as an experiment, the sample mean is   a random variable
23. In computing the standard error of the mean, the finite population correction factor is not used when    n/N ≤ 0.05
24. In point estimation, data from the  sample is used to estimate the population parameter
25. The basis for using a normal probability distribution to approximate the sampling distribution of is   the central limit theorem
26. The expected value of equals the mean of the population from which the sample is drawn  for any sample size
27. The expected value of the random variable is  μ
28. The fact that the sampling distribution of the sample mean can be approximated by a normal probability distribution whenever the sample size is large is based on the   central limit theorem
29. The finite correction factor should be used in the computation of when n/N is greater than   .05
30. The number of random samples (without replacement) of size 3 that can be drawn from a population of size 5 is 10
31. The population being studied is usually considered ______ if it involves an ongoing process that makes listing or counting every element in the population impossible. Infinite
32. The probability distribution of all possible values of the sample mean is called the  sampling distribution of the sample mean
33. The purpose of statistical inference is to provide information about the  population based upon information contained in the sample
34. The sample mean is the point estimator of   μ
35. The sample statistic s is the point estimator of   σ
36. The sampling distribution of the sample mean  is the probability distribution showing all possible values of the sample mean
37. The set of all elements of interest in a study is  a population
38. The standard deviation of a point estimator is the   standard error
39. The standard deviation of all possible values is called the   standard error of the mean
40. The standard deviation of is referred to as   standard error of the proportion
41. The standard deviation of is referred to as the   standard error of the mean
42. The value of the ___________ is used to estimate the value of the population parameter.  sample statistic
43. There are 6 children in a family. The number of children defines a population. The number of simple random samples of size 2 (without replacement) which are possible equals   15
44. Whenever the population has a normal probability distribution, the sampling distribution of is a normal probability distribution for   any sample size
45. Which of the following is(are) point estimator(s)?  s

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