Psy302 Quantitative Methods
QUIZ CHAPTER Seven
1. A distribution of all sample means or
sample variances that could be obtained in
samples of a given size from the same
population is called
A. a conditional procedure
B. a sampling distribution
C. sampling without
replacement
D. random sampling
E. all of the above
1. A distribution of all sample means or
sample variances that could be obtained in
samples of a given size from the same
population is called
A. a conditional procedure
B. a sampling distribution
C. sampling without
replacement
D. random sampling
E. all of the above
2. What is the central limit theorem?
A. It explains that sample means will vary minimally
from the population mean.
B. It explains that a sampling distribution of possible
sample means is approximately normally
distributed, regardless of the shape of the
distribution in the population.
C. It explains that if we select a sample at random,
then on average we can expect the sample mean
to exceed the population mean.
D. all of the above
2. What is the central limit theorem?
A. It explains that sample means will vary minimally
from the population mean.
B. It explains that a sampling distribution of
possible sample means is approximately
normally distributed, regardless of the shape of
the distribution in the population.
C. It explains that if we select a sample at random,
then on average we can expect the sample mean
to exceed the population mean.
D. all of the above
3. A sample statistic is an unbiased
estimator if its value equals the value of
the _____ on average.
A. proportion
B. p-value
C. parameter
D. mean
E. all of the above
3. A sample statistic is an unbiased
estimator if its value equals the value of
the _____ on average.
A. proportion
B. p-value
C. parameter
D. mean
E. all of the above
4. . It happens to be the case that the
standard error of the sampling distribution
of sample means
A.
is minimal
B.
is approximately equal to that in the
population
C.
gets larger as the sample size increases
D.
both A and C
4. . It happens to be the case that the
standard error of the sampling distribution
of sample means
A.
is minimal
B.
is approximately equal to that in the
population
C.
gets larger as the sample size increases
D.
both A and C
5. The mean of the sampling distribution of
sample means is
A. equal to the population mean
B. equal to the population variance
C. both A and B
D. none of the above
5. The mean of the sampling distribution of
sample means is
A. equal to the population mean
B. equal to the population variance
C. both A and B
D. none of the above
6. The Law of Large numbers states that
_____ the number of observations in a sample
will decrease the standard error.
6. The Law of Large numbers states that
_____ the number of observations in a sample
will decrease the standard error.
A. increasing
B. decreasing
C. multiplying
D. dividing
E. all of the above
6. The Law of Large numbers states that
_____ the number of observations in a sample
will decrease the standard error.
A. increasing
B. decreasing
C. multiplying
D. dividing
E. all of the above
7. If a random sample is selected from a
population with a mean equal to 15 then we
expect the value of the sample mean on
average to be:
A. greater than 15
B. less than 15
C. equal to 15
7. If a random sample is selected from a
population with a mean equal to 15 then we
expect the value of the sample mean on
average to be:
A. greater than 15
B. less than 15
C. equal to 15
8. In the bar graph below the vertical lines
(error bars) above the bars represent:
A. the mean
B. the standard
deviation
C. the variance
D. the correlation
E. SEM
8. In the bar graph below the vertical lines
(error bars) above the bars represent:
A. the mean
B. the standard
deviation
C. the variance
D. the correlation
E. SEM
9. The standard error of the mean tells us:
A. the value of the population mean.
B. the standard deviation of the sampling
distribution
C. how far possible sample means deviate from the
population mean.
D. how nasty the distribution is
E. b & c
9. The standard error of the mean tells us:
A. the value of the population mean.
B. the standard deviation of the sampling
distribution
C. how far possible sample means deviate from the
population mean.
D. how nasty the distribution is
E. b & c
10. _____ is the extent to which sample
means elected from the same population vary
from each other.
A. mean square
B. SEM
C. sampling error
D. the law of large
numbers
E. the central limit
theorem
10. _____ is the extent to which sample
means elected from the same population vary
from each other.
A. mean square
B. SEM
C. sampling error
D. the law of large
numbers
E. the central limit
theorem
The End