QUIZ CHAPTER Seven

Psy302 Quantitative Methods

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 calledA. a conditional

procedureB. a sampling distributionC. sampling without

replacementD. random samplingE. 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 calledA. a conditional

procedureB. a sampling

distributionC. sampling without

replacementD. random samplingE. 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. proportionB. p-valueC. parameterD. meanE. all of the above

3. A sample statistic is an unbiased estimator if its value equals the value of the _____ on average. A. proportionB. p-valueC. parameterD. meanE. all of the above

4. . It happens to be the case that the standard error of the sampling distribution of sample meansA. is minimalB. is approximately equal to

that in the populationC. gets larger as the sample

size increasesD. both A and C

4. . It happens to be the case that the standard error of the sampling distribution of sample meansA. is minimalB. is approximately equal to

that in the populationC. gets larger as the sample

size increasesD. both A and C

5. The mean of the sampling distribution of sample means is

A. equal to the population meanB. equal to the population

varianceC. both A and BD. 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 BD. 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. increasingB. decreasingC. multiplyingD. dividingE. 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. increasingB. decreasingC. multiplyingD. dividingE. 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 15B. less than 15C. 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 15B. less than 15C. equal to 15

8. In the bar graph below the vertical lines (error bars) above the bars represent: A. the meanB. the standard

deviationC. the varianceD. the correlationE. SEM

8. In the bar graph below the vertical lines (error bars) above the bars represent: A. the meanB. the standard

deviationC. the varianceD. the correlationE. SEM

9. The standard error of the mean tells us:

A. the value of the population mean.B. the standard deviation of the sampling

distributionC. how far possible sample means deviate

from the population mean.D. how nasty the distribution isE. 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

distributionC. how far possible sample means deviate

from the population mean.D. how nasty the distribution isE. b & c

10. _____ is the extent to which sample means elected from the same population vary from each other. A. mean squareB. SEMC. sampling errorD. the law of large

numbersE. the central limit

theorem

10. _____ is the extent to which sample means elected from the same population vary from each other. A. mean squareB. SEMC. sampling errorD. the law of large

numbersE. the central limit

theorem

The End