Chapter 7: Sampling and Sampling Distibutions

A probability distribution for all possible values of a sample statistic is known as
1. a sample statistic
2. a parameter
3. simple random sampling
4. a sampling distribution
5. none of the above
A population characteristic, such as a population mean, is called
1. a statistic
2. a parameter
3. a sample
4. none of the above
A property of a point estimator that occurs whenever larger sample sizes tend to provide point estimates closer to the population parameter is known as
1. efficiency
2. unbiased sampling
3. consistency
4. none of the above
Given two point estimators of a parameter, the point estimator with the smaller standard error is said to have the greater
1. coefficient of variation
2. average variance
3. relative efficiency
4. none of the above
A measure from a sample, such as a sample mean, is known as
1. a statistic
2. a parameter
3. mean deviation
4. none of the above
The standard deviation of a point estimator is called the
1. standard deviation
2. standard error
3. point estimator
4. none of the above
A theorem that allows us to use the normal probability distribution to approximate the sampling distribution of the sample mean whenever the sample size is large is known as the
1. approximation theorem
2. normal probability theorem
3. central limit theorem
4. none of the above
A property of a point estimator that occurs whenever the expected value of the point estimator is equal to the population parameter it estimates is known as
1. consistency
2. the expected value
3. the estimator
4. unbiased
5. none of the above
The number of different simple random samples of size 4 that can be selected from a population of size 6 is
1. 24
2. 30
3. 15
4. 4
5. none of the above
In computing the standard error of the mean, the finite population correction factor is used when
1. n/N > 30
2. N/n < 0.05
3. n/N < 0.05
4. n/N > 0.05
5. none of the above