Suppose You Drew A Random Sample From A Population.
Suppose You Drew A Random Sample From A Population.
1. Suppose you drew a random sample from a population where the mean is 100. The standard error of the sampling distribution is 10. The mean for your sample is 80. What could you conclude about your sample?
A. The sample mean does not occur very often by chance in the sampling distribution of means and probably did not come from the given population.
B. The sample mean occurs very often by chance in the sampling distribution of means and probably did not come from the given population.
C. The sample mean does not occur very often by chance in the sampling distribution of means but probably did come from the given population.
D. The sample mean occurs very often by chance in the sampling distribution of means and probably did come from the given population.
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2. What do we call that portion of the sampling distribution in which values are considered too unlikely to have occurred by chance?
A. Region of criterion value
B. Region of critical value
C. Region of rejection
D. Critical value
3. Suppose you take a piece of candy out of a jar, look to determine its color, then put it back into the jar before you randomly select the next piece of candy. This type of sampling is called
A. an independent event.
B. sampling with replacement.
C. a dependent event.
D. sampling without replacement.
4. There are 26 red cards in a playing deck and 26 black cards. The probability of randomly selecting a red card or a black card is 26/52 = 0.50. Suppose you randomly select a card from the deck five times, each time replacing the card and reshuffling before the next pick. Each of the five selections has resulted in a red card. On the sixth turn, the probability of getting a black card
A. has got to be low because you’ve gotten so many red cards on the previous turns.
B. has got to be high because you’ve gotten so many red cards on the previous turns.
C. is the same as it has always been if the deck is a fair deck.
D. needs to be recomputed because you are sampling with replacement.
5. What can you conclude about a sample mean that falls within the region of rejection?
A. The sample probably represents some population other than the one on which the sampling distribution was based.
B. The sample represents the population on which the sampling distribution was based.
C. Another sample needs to be collected.
D. The sample should have come from the given population.
6. What can we conclude when the absolute value of a z-score for a sample mean is larger than the critical value?
A. The random selection procedure was conducted improperly.
B. The sample mean is reasonably likely to have come from the given population by random sampling.
C. The sample mean represents the particular raw score population on which the sampling distribution is based.
D. The sample mean does not represent the particular raw score population on which the sampling distribution is based.
7. When rolling a pair of fair dice, the probability of rolling a total point value of “7” is 0.17. If you rolled a pair of dice 1,000 times and the point value of “7” appeared 723 times, what would you probably conclude?
A. This is not so unlikely as to make you doubt the fairness of the dice.
B. Although not impossible, this outcome is so unlikely that the fairness of these dice is questionable.
C. Since the total point value of “7” has the highest probability of any event in the sampling distribution, this is an extremely likely outcome.
D. It is impossible for this to happen if the dice are fair.
8. What is the appropriate outcome of a z-test?
A. Reject and accept
B. Reject and accept
C. Reject ; accept
D. Fail to reject ; accept
9. The null hypothesis describes the
A. sample statistic and the region of rejection.
B. sample statistic if a relationship does not exist in the sample.
C. population parameters represented by the sample data if the predicted relationship exists.
D. population parameters represented by the sample data if the predicted relationship does not exist.
10. In a one-tailed test, is significant only if it lies
A. nearer µ than and has a different sign from
B. in the tail of the distribution beyond and has a different sign from
C. nearer µ than and has the same sign as
D. in the tail of the distribution beyond and has the same sign as
11. The key difference between parametric and nonparametric procedures is that parametric procedures
A. do not require that stringent assumptions be met.
B. require that certain stringent assumptions be met.
C. are used for population distributions that are skewed.
D. are used for population distributions that have nominal scores.
12. Which of the following accurately defines a Type I error?
A. Rejecting when is true
B. Rejecting when is false
C. Retaining when is true
D. Retaining when is false
13. If and what is the value of
14. What happens to the probability of committing a Type I error if the level of significance is changed from a=0.01 to a=0.05?
A. The probability of committing a Type I error will decrease.
B. The probability of committing a Type I error will increase.
C. The probability of committing a Type I error will remain the same.
D. The change in probability will depend on your sample size.
15. Suppose you perform a two-tailed significance test on a correlation between the number of books read for enjoyment and the number of credit hours taken, using 32 participants. Your is –0.15, which is not a significant correlation coefficient. Which of the following is the correct way to report this finding?
A. r(32) = –0.15, p > 0.05
B. r(31) = –0.15, p > 0.05
C. r(30) = –0.15, p < 0.05
D. r(30) = –0.15, p > 0.05
16. Which of the following would increase the power of a significance test for correlation?
A. Changing a from 0.05 to 0.01
B. Increasing the variability in the Y scores
C. Changing the sample size from N = 25 to N = 100
D. Changing the sample size from N = 100 to N = 25
17. If a sample mean has a value equal to µ, the corresponding value of t will be equal to
18. What is ?
A. The estimated population standard deviation
B. The population standard deviation
C. The estimated standard error of the mean
D. The standard error of the mean
19. In a one-tailed significance test for a correlation predicted to be positive, the null
hypothesis is ___________ and the alternative hypothesis is __________.
A. Ho: ρ ≤ 0; Ha: ρ > 0
B. Ho: ρ < 0; Ha: ρ ≥ 0
C. Ho: ρ = 0; Ha: ρ > 0
D. Ho: ρ < 0; Ha ρ > 0
20. How is the t-test for related samples performed?
A. By conducting a one-sample t-test on the sample of difference scores
B. By conducting an independent samples t-test on the sample of difference scores
C. By converting the scores to standard scores and then performing a related samples t-test
D. By measuring the population variance and testing it using an independent samples t-test
21. What does the alternative hypothesis state in a two-tailed independent samples
22. One way to increase power is to maximize the difference produced by the two conditions in the experiment. How is this accomplished?
A. Change a from 0.05 to 0.01.
B. Change the size of N from 100 to 25.
C. Design and conduct the experiment so that all the subjects in a sample are treated in a consistent manner.
D. Select two very different levels of the independent variable that are likely to produce a relatively large difference between the means.
23. Suppose you perform a two-tailed independent samples t-test, using a = 0.05, with 15 participants in one group and 16 participants in the other group. Your is 4.56, which is significant. Which of the following is the correct way to report this finding?
A. t(31) = 4.56; p< 0.05
B. t(29) = 4.56; p < 0.05
C. t(29) = 4.56; p > 0.05
D. t(29) = 4.56; p = 0.05
24. Suppose that you measure the IQ of 14 subjects with short index fingers and the IQ
of 14 subjects with long index fingers. You compute an independent samples t-test,
and the is 0.29, which is not statistically significant. Which of the following is the
most appropriate conclusion?
A. There is no relationship between length of index finger and IQ.
B. There is a relationship between length of index finger and IQ.
C. The relationship between length of index finger and IQ does not exist.
D. We do not have convincing evidence that our measured relationship between length of index finger and IQ is due to anything other than sampling error.
25. The assumptions of the t-test for related samples are the same as those for the test for independent samples except for requiring
A. that the dependent variable be measured on an interval or ratio scale.
B. that the population represented by either sample form a normal distribution.
C. homogeneity of variance.
D. that each score in one sample be paired with a particular score in the other sample.
Use SPSS and the provided data set to answer the questions below:
26. Test the age of the participants (AGE1) against the null hypothesis H 0 = 34. Use a
one-sample t-test. How would you report the results?
A. t = -1.862, df = 399, p >.05
B. t = -1.862, df = 399, p <.05
C. t = 1.645, df = 399, p >.05
D. t = 1.645, df = 399, p <.05
27. Test to see if there is a significant difference between the age of the participant and the age of the partner. Use a paired-sample t-test and an alpha level of 1%. How would you interpret the results of this test?
A. The partners are significantly older than the participants.
B. The partners are significantly younger than the participants
C. The age of the participants and partners are not significantly different.
D. Sometimes the partners are older, sometimes the participants are older.
28. Look at the correlation between Risk-Taking (R) and Relationship Happiness (HAPPY). Use the standard alpha level of 5%. How would you describe the relationship?
A. The relationship is non-significant.
B. There is a significant negative relationship.
C. There is a significant positive relationship.
D. The correlation is zero.
29. If you randomly chose someone from this sample, what is the chance that they
described their relationship as either Happy or Very Happy?
30. Perform independent sample t-tests on the Lifestyle, Dependency, and Risk-Taking
scores (L, D, and R) comparing men and women (GENDER1). Use p < .05 as your
alpha level. On each of the three scales, do men or women have a significantly
A. Lifestyle: Men, Dependency: Women, Risk-Taking: Men.
B. Lifestyle: Not significantly different, Dependency: Women, Risk-Taking: Men
C. Lifestyle: Women, Dependency: Women, Risk-Taking: Men
D. Lifestyle: Men, Dependency: Men, Risk-Taking: Not significantly different