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Questions in this quiz (60)
11. The distribution of sample means is:
- Aa. The collection of all random scores for all possible samples of a given size n from a population.
- Bb. The collection of all sample means for all possible random samples of a given size n from a population.
- Cc. The collection of all scores for a sample of random means of a given size n from a population.
- Dd. None of the above.
- Aa. The collection of all random scores for all possible samples of a given size n from a population.
22. A measure of variation of the distribution of sample means is:
- Aa. The same as the standard deviation of the population
- Bb. Is called the standard error of the mean
- Cc. Is independent of the size of the sample
- Dd. Both b and c.
- Aa. The same as the standard deviation of the population
33. For the general population, mean IQ is 100 with a standard deviation of 15. A sample of 100 people is selected at random from the population, with a sample mean of 102. This sample mean comes from a distribution of sample means with the following properties:
- Aa. a mean of 100 and a standard error of 1.5
- Bb. a mean of 102 and a standard error of 1.5
- Cc. a mean of 100 and a standard error of 15
- Dd. a mean of 102 and a standard error of 15
- Aa. a mean of 100 and a standard error of 1.5
44. The Satisfaction with Life (SWL) questionnaire produces data that is typically highly negatively skewed, with a long tail towards low scores on the scale. A psychologist takes a random sample of 30 people from the general community and asks them to complete the SWL questionnaire. The sample mean can be considered to come from a distribution that is:
- Aa. highly negatively skewed
- Bb. positively skewed to counteract the negative skew
- Cc. normally distributed
- Dd. a sample size of 30 is not large enough to be sure what is the distribution.
- Aa. highly negatively skewed
55. A researcher wishes to infer a population mean from a sample of 25. She is not confident about her estimate and wants to be more precise. What would you advise her?
- Aa. her estimate must be accurate, given the sample size of 25
- Bb. as every sample mean is different, she needs to take many samples to be very precise
- Cc. the central limit theorem assures her that the population mean is the same as her sample mean
- Dd. if she wants to improve her precision she should use a larger sample size.
- Aa. her estimate must be accurate, given the sample size of 25
66. For a normal distribution:
- Aa. 75% of observations are within one standard deviation of the mean
- Bb. 68% of observations are within two standard deviations of the mean
- Cc. 95% of observations are within one standard deviation of the mean
- Dd. none of the above.
- Aa. 75% of observations are within one standard deviation of the mean
77. A person has an IQ with a z-score more than +2.
- Aa. That person is an extreme case. There is a high probability of seeing a z-score of that size.
- Bb. That person is not considered an extreme case. There is a low probability of seeing a z-score of that size.
- Cc. That person is not considered an extreme case. There is a high probability of seeing a z-score of that size.
- Dd. That person is an extreme case. There is a low probability of seeing a z-score of that size.
- Aa. That person is an extreme case. There is a high probability of seeing a z-score of that size.
88. The normal distribution is:
- Aa. extremely common for actual psychological measures
- Bb. useful for statistical inference because of the Central Limit theorem
- Cc. not at all common in actual psychological measures
- Dd. is common in actual psychology but only when it is skewed.
- Aa. extremely common for actual psychological measures
99. As the sample size gets larger:
- Aa. There is more variability in the population.
- Bb. The standard error increases by a factor related to the sample size.
- Cc. The standard error decreases.
- Dd. The precision of estimation decreases.
- Aa. There is more variability in the population.
1010. To estimate a population mean
- Aa. Use a sample mean but only if the population distribution is normal.
- Bb. Use a sample standard deviation to increase the precision.
- Cc. Calculate a standard error and then increase the sample size.
- Dd. Use a sample mean, preferably with a sample of reasonable size.
- Aa. Use a sample mean but only if the population distribution is normal.
1111. Which option makes the most sense?
- Aa. A sample is a set of individuals selected (usually randomly) from the population. A population is every possible individual of relevance to the research question. μ is notation for the mean of the sample (a sample statistic) while M is for the mean of the population (a population parameter).
- Bb. A sample is a set of individuals selected (usually randomly) from the population. A population is every possible individual of relevance to the research question. M is notation for the mean of the sample (a sample parameter) while μ is for the mean of the population (a population statistic).
- Cc. A population is a set of individuals selected (usually randomly) from the sample. A sample is every possible individual of relevance to the research question. M is notation for the mean of the sample (a sample statistic) while μ is for the mean of the population (a population parameter).
- Dd. A sample is a set of individuals selected (usually randomly) from the population. A population is every possible individual of relevance to the research question. M is notation for the mean of the sample (a sample statistic) while μ is for the mean of the population (a population parameter).
- Aa. A sample is a set of individuals selected (usually randomly) from the population. A population is every possible individual of relevance to the research question. μ is notation for the mean of the sample (a sample statistic) while M is for the mean of the population (a population parameter).
1212. A sample of 25 university students is chosen from the general population and IQ is measured. Mean IQ for the sample is 103. For the general population, mean IQ is 100 with a standard deviation of 15.
- Aa. Because the mean of the sample is higher than the population mean, we can infer that the mean IQ of university students is significantly higher than that of the general population.
- Bb. Because the mean IQ of the sample is 0.2 standard deviations above the mean IQ of the population, we can infer that the mean IQ of university students is significantly higher than that of the general population.
- Cc. Because the mean IQ of the sample is 0.2 standard deviations above the mean IQ of the population, we can infer that the mean IQ of university students is NOT significantly higher than that of the general population.
- Dd. Because the mean IQ of the sample is 1 standard error above the population mean, we can infer that the mean IQ of university students is NOT significantly higher than that of the general population.
- Aa. Because the mean of the sample is higher than the population mean, we can infer that the mean IQ of university students is significantly higher than that of the general population.
1313. The null hypothesis is:
- Aa. What the researcher believes is true about the data.
- Bb. The hypothesis that says there is no effect to observe.
- Cc. What the researcher believes is true about the population.
- Dd. The hypothesis that says there is likely to be an effect.
- Aa. What the researcher believes is true about the data.
1414. The alpha-level is:
- Aa. The probability used to define very unlikely samples if the null hypothesis is true.
- Bb. The probability used to define very unlikely samples if the null hypothesis is false.
- Cc. The probability used to define likely samples if the null hypothesis is true.
- Dd. The probability used to define likely samples if the null hypothesis is false.
- Aa. The probability used to define very unlikely samples if the null hypothesis is true.
1515. In using a z-score of approximately 2 (to be more precise, 1.96) in a single sample z- test, we are saying that:
- Aa. the alpha level is 5% and we reject the null hypothesis if the probability is greater than this.
- Bb. the alpha level is 2.5% and we reject the null hypothesis if the probability is less than this.
- Cc. the alpha level is 5% and we reject the null hypothesis if the probability is less than this.
- Dd. the alpha level is 2.5% and we reject the null hypothesis if the probability is greater than this.
- Aa. the alpha level is 5% and we reject the null hypothesis if the probability is greater than this.
1616. A researcher is interested in the effects of a drug on physical activity levels. The researcher has designed a scale for activity levels that in the general population has a mean of 18 and a standard deviation of 4. On the scale a higher score indicates higher activity. A random sample of 16 people is given the drug. The mean activity levels of the sample in the week after the administration of the drug is 15. The null hypothesis is:
- Aa. μ0=18
- Bb. μ0=15
- Cc. μ0=18/√4 = 9
- Dd. μ0= 4/√4 = 1
- Aa. μ0=18
171. A researcher is interested in the effects of a drug on physical activity levels. The researcher has designed a scale for activity levels that in the general population has a mean of 18 and a standard deviation of 4. On the scale a higher score indicates higher activity. A random sample of 16 people is given the drug. The mean activity levels of the sample in the week after the administration of the drug is 15. The null hypothesis is:
a. μ0=18
2. In regard to the research of question 6, assuming an alpha level of 5%, the z-score for the sample is:- Aa. 9
- Bb. – 3
- Cc. 1
- Dd. – 3/4
- Aa. 9
1818. In regard to the research of question 16, assuming an alpha level of 5%, the correct inference is:
- Aa. Reject the null hypothesis. There is evidence from this study that the drug decreases activity.
- Bb. Do not reject the null hypothesis. There is no evidence from this study that the drug decreases activity.
- Cc. Reject the null hypothesis. There is evidence from this study that the drug increases activity.
- Dd. Do not reject the null hypothesis. There is evidence from this study that the drug decreases activity.
- Aa. Reject the null hypothesis. There is evidence from this study that the drug decreases activity.
1919. What makes the most sense?
- Aa. If there is a high probability, reject the null hypothesis. There is evidence for an effect.
- Bb. If there is a low probability, reject the null hypothesis. There is evidence for an effect.
- Cc. If there is a high probability, reject the null hypothesis. There is no evidence for an effect.
- Dd. If there is a low probability, reject the null hypothesis. There is no evidence for an effect.
- Aa. If there is a high probability, reject the null hypothesis. There is evidence for an effect.
2020. A single sample z-test:
- Aa. involves calculating a z-score for the population mean, assuming that the standard deviation is obtained from the sample. It compares the sample mean with the population mean.
- Bb. involves calculating a z-score for the sample mean, assuming that the standard deviation is estimated from the sample. It compares the sample mean with a given number.
- Cc. involves calculating a z-score for the sample mean. This requires that the standard deviation of the population be known. It compares the sample mean with the population mean.
- Dd. involves calculating a z-score for the sample mean. This requires that the standard deviation of the population be known. It compares the sample mean with a given number.
- Aa. involves calculating a z-score for the population mean, assuming that the standard deviation is obtained from the sample. It compares the sample mean with the population mean.
2121. What is the difference between a single sample z-test and a single sample t-test?
- Aa. The z-test uses the mean and the t-test uses the standard deviation.
- Bb. The z-test requires knowledge about the population mean, and the t-test does not.
- Cc. The z-test depends on the size of the sample but the t-test does not.
- Dd. The z-test requires knowledge about the population standard deviation and the t-test does not.
- Aa. The z-test uses the mean and the t-test uses the standard deviation.
2222. A random sample of 25 university students has a mean IQ of 104 and a standard deviation of 10. For the general population, mean IQ is 100. A single sample t-test is conducted to seek evidence whether the mean IQ of students in general is higher than the general population.
- Aa. The estimated standard error of the mean is 2 and the degrees of freedom are 25.
- Bb. The estimated standard error of the mean is 2 and the degrees of freedom are 24.
- Cc. The estimated standard error of the mean is 3 and the degrees of freedom are 25.
- Dd. The estimated standard error of the mean is 3 and the degrees of freedom are 24.
- Aa. The estimated standard error of the mean is 2 and the degrees of freedom are 25.
2323. In a single sample t-test the calculated t-statistic for a sample of 4 people is 2.8. A researcher wishes to decide where this value is significant at a 10% alpha level, using both tails (i.e. top and bottom) of the t-distribution.
- Aa. df = 3. The t-value is not significant because it is less than 3.182.
- Bb. df = 4. The t-value is significant because it is greater than 2.776.
- Cc. df = 4. The t-value is significant because it is greater than 2.132.
- Dd. df = 3. The t-value is significant because it is greater than 2.353.
- Aa. df = 3. The t-value is not significant because it is less than 3.182.
2424. A sample of 10 children with specific brain injury has a mean IQ of 94.2 and a standard deviation of 6.2. The null hypothesis is that the population of children with this brain injury has a mean IQ of 100.
- Aa. The calculated t-value is approximately – 1.9. This is not extreme because it is less than 2.262, so do not reject the null hypothesis.
- Bb. The calculated t-value is approximately – 2.98. This is extreme because it is more than 2.262, so reject the null hypothesis.
- Cc. The calculated t-value is approximately – 1.9. This is extreme because it is less than 2.262, so reject the null hypothesis.
- Dd. The calculated t-value is approximately – 2.98. This is not extreme because it is more than 2.262, so reject the null hypothesis.
- Aa. The calculated t-value is approximately – 1.9. This is not extreme because it is less than 2.262, so do not reject the null hypothesis.
2525. A psychologist has prepared an “Optimism test” that is administered yearly to graduating students. The test measures how students feel about their futures – the higher the score, the more optimistic. Last year’s class had a mean score of 15. A sample of 9 students is selected from this year’s class. The sample mean is 10 and the estimated standard error is 1.14. The psychologist wishes to determine whether there has been a significant fall in the mean level of optimism compared to last year.
- Aa. The null hypothesis is that mean optimism this year is 10, and the degrees of freedom are 9.
- Bb. The null hypothesis is that mean optimism this year is 15, and the degrees of freedom are 9.
- Cc. The null hypothesis is that mean optimism this year is 10, and the degrees of freedom are 8.
- Dd. The null hypothesis is that mean optimism this year is 15, and the degrees of freedom are 8.
- Aa. The null hypothesis is that mean optimism this year is 10, and the degrees of freedom are 9.
26(A psychologist has prepared an “Optimism test” that is administered yearly to graduating students. The test measures how students feel about their futures – the higher the score, the more optimistic. Last year’s class had a mean score of 15. A sample of 9 students is selected from this year’s class. The sample mean is 10 and the estimated standard error is 1.14. The psychologist wishes to determine whether there has been a significant fall in the mean level of optimism compared to last year.
a. The null hypothesis is that mean optimism this year is 15, and the degrees of freedom are 8.)
26. For the sample in question 25, the t-statistic is- Aa. 4.39
- Bb. – 4.39
- Cc. 8.77
- Dd. – 8.77
- Aa. 4.39
2727. For a sample of 9, based on the t-distribution table, with an alpha of 0.05 (two tails), the critical value is:
- Aa. 2.262
- Bb. – 2.262
- Cc. 2.306
- Dd. – 2.306
- Aa. 2.262
28(1. A psychologist has prepared an “Optimism test” that is administered yearly to graduating students. The test measures how students feel about their futures – the higher the score, the more optimistic. Last year’s class had a mean score of 15. A sample of 9 students is selected from this year’s class. The sample mean is 10 and the estimated standard error is 1.14. The psychologist wishes to determine whether there has been a significant fall in the mean level of optimism compared to last year.
a. The null hypothesis is that mean optimism this year is 15, and the degrees of freedom are 8.)
28. For the sample in question 25, at a 5% alpha level the correct inference is:- Aa. reject the null hypothesis, there is no evidence for a significant difference in optimism compared to the previous year.
- Bb. do not reject the null hypothesis, there is no evidence for a significant difference in optimism compared to the previous year.
- Cc. reject the null hypothesis, there is evidence for a significant difference in optimism compared to the previous year.
- Dd. do not reject the null hypothesis, there is evidence for a significant difference in optimism compared to the previous year.
- Aa. reject the null hypothesis, there is no evidence for a significant difference in optimism compared to the previous year.
2929. The correct way to report the statistics from this result is:
- Aa. t(2.306)=9, p<.05
- Bb. t(9)=2.306, p>.05
- Cc. t(8)= .– 4.39, p<.05
- Dd. t(9)=.05, p<.05
- Aa. t(2.306)=9, p<.05
3030. The z-test cannot be used when:
- Aa. you do not know the population standard deviation
- Bb. when you want to compare the means of two different samples
- Cc. when you want to compare change in a sample across time
- Dd. all of the above
- Aa. you do not know the population standard deviation
3131. An IQ scale can be considered:
- Aa. a nominal scale of measurement
- Bb. a numerical scale which has a real zero
- Cc. a discrete measurement scale
- Dd. a continuous scale.
- Aa. a nominal scale of measurement
3232. Which answer makes the most sense:
- Aa. One of our goals in empirical psychology is to infer information about a population using a sample. We use sample statistics to estimate population parameters.
- Bb. One of our goals in empirical psychology is to infer information about a sample using a population. We use sample statistics as estimates of population parameters.
- Cc. One of our goals in empirical psychology is to infer information about a population using a sample. We use population statistics as estimates from sample parameters.
- Dd. One of our goals in empirical psychology is to infer information about populations and samples. We use population statistics as estimates from sample parameters.
- Aa. One of our goals in empirical psychology is to infer information about a population using a sample. We use sample statistics to estimate population parameters.
3333. Arrival time in class is approximately normally distributed. For a given lecture, the mean arrival time is 2 minutes after the advertised start time of the lecture with a standard deviation of 5 minutes. We expect that:
- Aa. about 5% of students will arrive 8 minutes before the advertised time
- Bb. about 5% of students will arrive 10 minutes before the advertised time
- Cc. about 2.5% of students will arrive 12 minutes after the advertised time
- Dd. about 2.5% of students will arrive 8 minutes after the advertised time
- Aa. about 5% of students will arrive 8 minutes before the advertised time
3434. A wonderful Olympic swimmer wins three gold medals. In the first final, the swimmer completed in 67 seconds while the mean finishing time was 70 seconds (SD=2 seconds); in the second final, the swimmer completed in 2minutes, 14 seconds while the mean time was 2.5 minutes (SD= 10 seconds); in the third final the swimmer completed in 20 minutes while the mean was 23 minutes (SD=45 seconds). Comparing all three finals, how well did the swimmer perform relative to all other finalists?
- Aa. The swimmer did best in the first final, followed by the second and then the third.
- Bb. It is impossible to make a comparison
- Cc. The swimmer did best in the third final, and beat the field by slightly more in the second final than the first.
- Dd. The swimmer did best in the second final, followed by the first and then the third.
- Aa. The swimmer did best in the first final, followed by the second and then the third.
3535. A clinical psychologist devises a new psychological scale that measures foolishness. A high score on the scale indicates a foolish person. The scale is positively skewed with a long tail towards the higher scores. The minimum score on the scale is 10, the mean is 20 and the standard deviation is 10.
- Aa. Because of the long tail, there are many people who are foolish and few people get moderate or low scores.
- Bb. Unusual cases are quite rare, so that we expect 95% of people to have a score between 0 and 40.
- Cc. Because the standard deviation is one-half of the mean (i.e. around 1.96), the distribution is normally distributed.
- Dd. There are few people who are foolish and it is not unusual to get moderate or low scores.
- Aa. Because of the long tail, there are many people who are foolish and few people get moderate or low scores.
3636. The following are measures of variation in a distribution:
- Aa. Variance, standard deviation, mean, range.
- Bb. Standard deviation, range, mean.
- Cc. Variance, standard deviation, range.
- Dd. Standard deviation, skew, normal, symmetric.
- Aa. Variance, standard deviation, mean, range.
3737. IQ is normally distributed with a mean of 100 and a standard deviation of 15. George has an IQ of 85. His z-score is:
- Aa. 1.0
- Bb. – 1.0
- Cc. 0.15
- Dd. – 0.85
- Aa. 1.0
38Neuroticism and extraversion are said to be two important personality dimensions. A researcher collects data on a small sample of 6 males and 6 females and measures these two dimensions, labeled as N and E, respectively. Here is some PASW output from the Explore command:
- Aa. The mean neuroticism score for males in this sample is 33.0, and for females is 33.2.
- Bb. The extraversion scores for males are more widely spread out than for females.
- Cc. Extraversion scores for males are more tightly grouped around the mean than for females.
- Dd. Neuroticism is highly skewed.
- Aa. The mean neuroticism score for males in this sample is 33.0, and for females is 33.2.
3939. For the data of question 38, here are the boxplots for extraversion:
- Aa. On average in this sample, females have higher extraversion than males.
- Bb. On average in this sample, males have higher extraversion than females.
- Cc. Female extraversion in this sample has a larger range.
- Dd. There is a significant difference between males and females.
- Aa. On average in this sample, females have higher extraversion than males.
4040. For the data of question 38, here are the boxplots for neuroticism:
- Aa. On average in this sample, females have higher neuroticism scores than males.
- Bb. On average in this sample, males have higher neuroticism scores than females.
- Cc. There are more outliers in the male sample.
- Dd. The middle 50% of the data indicates that the similarity between males and females for this sample is significant.
- Aa. On average in this sample, females have higher neuroticism scores than males.
4141. A repeated measures research design:
- Aa. involves measuring the same construct across two different samples at two time points
- Bb. involves measuring the same construct across the same sample at two different time points
- Cc. is analysed using a t-test because it involves comparing a single sample repeatedly with the population mean (e.g. 100 for IQ)
- Dd. is analysed using a t-test because the null hypothesis is that there is no difference between the two independent groups across time.
- Aa. involves measuring the same construct across two different samples at two time points
4242. An independent groups research design
- Aa. involves measuring the same construct across two different samples
- Bb. involves measuring the same construct at two different time points across an independent sample
- Cc. is analysed using a t-test because it involves comparing the means of two groups with a given number (.e.g. 100 for IQ)
- Dd. is analysed using a t-test because the null hypothesis is that there is a difference between the two groups.
- Aa. involves measuring the same construct across two different samples
4343. Which answer makes the most sense?
- Aa. Data for an independent groups t-test involves two variables, both continuous and measured at separate time points. The null hypothesis is that there is no difference in means across time.
- Bb. Data for an independent groups t-test involves three variables, one continuous and the other two to indicate which group the participant is in. The null hypothesis is that there is no difference in means between the two groups.
- Cc. Data for an independent groups t-test involves two variables, one continuous and the other to indicate which group the participant is in. The null hypothesis is that there is a difference in means between the two groups.
- Dd. Data for an independent groups t-test involves two variables, one continuous and the other a grouping variable. The null hypothesis is that there is no difference in means between the two groups.
- Aa. Data for an independent groups t-test involves two variables, both continuous and measured at separate time points. The null hypothesis is that there is no difference in means across time.
4444. A researcher collects data on a sample of 10 children with specific brain injury. The researcher has a treatment that is intended to increase IQ for these children. Before treatment, the mean IQ for the sample is 97 with a standard deviation of 5. The mean IQ for the sample after treatment is 102, also with a standard deviation of 5.
- Aa. The researcher can use a single sample t-test to infer whether prior to treatment the brain injury has affected IQ relative to the general population, and a repeated measures t-test to infer whether the treatment improves IQ.
- Bb. The researcher can use an independent groups t-test to infer whether prior to treatment the brain injury affects IQ relative to the general population, and a repeated measures t-test to infer whether the treatment improves IQ.
- Cc. The researcher can use a single sample t-test to infer whether prior to treatment the brain injury affects IQ relative to the general population, and an independent groups t-test to infer whether the treatment improves IQ.
- Dd. The researcher can use a repeated measures t-test to infer whether prior to treatment the brain injury affects IQ relative to the general population, and a single sample t-test to infer whether the treatment improves IQ.
- Aa. The researcher can use a single sample t-test to infer whether prior to treatment the brain injury has affected IQ relative to the general population, and a repeated measures t-test to infer whether the treatment improves IQ.
4545. A psychologist has prepared an “Optimism test” that is administered yearly to graduating students. The test measures how students feel about their futures – the higher the score, the more optimistic. At the beginning of their final year, an initial sample of 9 Arts students had a mean score of 15. The researcher was only able to measure a sample of 9 Commerce students at the end of the year. They had a sample mean of 10.
- Aa. Two single sample t-tests will reveal whether the means differ from 15.
- Bb. A repeated measures t-test is needed to infer whether there is a decline from the beginning of the year to the end of the year.
- Cc. Because the data is across time and across groups, either a repeated measures or an independent groups t-test would determine whether there is a difference.
- Dd. An independent groups t-test is needed to infer whether there is a difference between Arts and Commerce students, leaving to one side the question of the time of measurement.
- Aa. Two single sample t-tests will reveal whether the means differ from 15.
4646. We have a small sample of 10 school children who have experienced a particular type of head injury. We suppose that the head injury might affect their IQ levels. We wish to see whether there is enough evidence in the sample to support this conclusion. Most IQ tests are standardized to have a mean of 100 and a standard deviation of 15, and have a normal distribution in the general population. We want to know whether the population of children with this head injury has a mean that is less 100. Here is the PASW output.
- Aa. There is evidence that mean IQ for children with this type of brain injury is significantly lower than that of the general population (t(9)=0.016, p<.05).
- Bb. There is evidence that mean IQ for children with this type of brain injury is significantly lower than that of the general population (t(9)=-3.0, p=0.02).
- Cc. There is no evidence that mean IQ for children with this type of brain injury is lower than that of the general population (t(9)=-3.0, p>.05).
- Dd. There is evidence that mean IQ for children with this type of brain injury is significantly lower than that of the general population (t(9)=-5.8, p=0.02).
- Aa. There is evidence that mean IQ for children with this type of brain injury is significantly lower than that of the general population (t(9)=0.016, p<.05).
4747. A student analyses the data from the study described in question 46 to produce the following output. What is wrong with this analysis?
- Aa. The null hypothesis is wrong. The null hypothesis population mean should not be 0.
- Bb. The t-value is too large for the degrees of freedom, so the probability cannot be assessed.
- Cc. The null hypothesis is not rejected.
- Dd. The mean difference is equal to the mean of the sample.
- Aa. The null hypothesis is wrong. The null hypothesis population mean should not be 0.
4848. As a brilliant and innovative cognitive neuropsychologist, you design a world leading intervention to increase IQ for children with this type of head injury. The intervention takes to the form of an extended one-on-one training course. You have a small sample of children. You measure their IQ before and after your intervention. Here is the PASW output from your analysis:
- Aa. Because the probability of 0.024 is small, do not reject the null hypothesis. The mean difference of 3.1 indicates a significant improvement in IQ due to the training.
- Bb. Because the t-value of 2.72 is large, do not reject the null hypothesis. The mean difference of 3.1 indicates a significant improvement in IQ due to the training.
- Cc. Because the probability of 0.024 is small, reject the null hypothesis. The mean difference of 3.1 indicates a significant improvement in IQ due to the training.
- Dd. Because the t-value of 0.024 is small, reject the null hypothesis. The mean difference of 3.1 indicates a significant improvement in IQ due to the training.
- Aa. Because the probability of 0.024 is small, do not reject the null hypothesis. The mean difference of 3.1 indicates a significant improvement in IQ due to the training.
4949. You also investigate your intervention using a control group design. In the treatment condition, you provide one-on-one training to a sample of 5 children with head injury. In the control condition, you provide a one-on-one play condition (i.e. no training) to a different sample of 5 children with head injury. Here is your PASW output:
- Aa. Because the probability of 0.014 is small, do not reject the null hypothesis. The mean difference of 3.1 indicates a significant improvement in IQ due to the training.
- Bb. Because the t-value of 3.1 is large, do not reject the null hypothesis. There is a significant difference in IQ between the two groups.
- Cc. Because the probability of 0.014 is less than the alpha level, reject the null hypothesis. There is a significant difference in IQ between the two groups.
- Dd. Because the t-value of 0.94 is large, do not reject the null hypothesis. There is no significant difference in IQ between the two groups.
- Aa. Because the probability of 0.014 is small, do not reject the null hypothesis. The mean difference of 3.1 indicates a significant improvement in IQ due to the training.
5050. Pick the best option.
- Aa. If a sample mean is expected (i.e. not extreme) assuming a null hypothesis, then there is evidence that the null hypothesis may not hold. After setting an α-level (usually 0.05), if the probability of the sample mean is less than α, reject the null hypothesis.
- Bb. If a sample mean is extreme assuming a null hypothesis, then there is evidence that the null hypothesis may not hold. After setting an α-level (usually 0.05), if the probability of the sample mean is more than α, reject the null hypothesis.
- Cc. If a sample mean is extreme assuming a null hypothesis, then there is evidence that the null hypothesis may not hold. After setting an α-level (usually 0.05), if the probability of the sample mean is less than α, reject the null hypothesis.
- Dd. If a sample mean is extreme assuming a null hypothesis, then there is no evidence that the null hypothesis may not hold. After setting an α-level (usually 0.05), if the probability of the sample mean is less than α, do not reject the null hypothesis.
- Aa. If a sample mean is expected (i.e. not extreme) assuming a null hypothesis, then there is evidence that the null hypothesis may not hold. After setting an α-level (usually 0.05), if the probability of the sample mean is less than α, reject the null hypothesis.
5151. Which answer makes the most sense?
- Aa. In a positive correlation, two variables tend to move in the same direction, so that an increase in one variable is associated with a decrease in the other.
- Bb. In a negative correlation, two variables tend to move in the same direction, so that an increase in one variable is associated with a decrease in the other.
- Cc. In a positive correlation, two variables tend to move in the same direction, so that an increase in one variable is associated with an increase in the other.
- Dd. In a negative correlation, two variables tend to move in the opposite direction, so that an increase in one variable is associated with an increase in the other.
- Aa. In a positive correlation, two variables tend to move in the same direction, so that an increase in one variable is associated with a decrease in the other.
5252. For a large sample, the following is the PASW output for a scatterplot of the personality dimension of extraversion versus neuroticism:
- Aa. There seems to be a strong positive correlation between the two personality dimensions.
- Bb. It is impossible to say from the scatterplot whether there is a correlation between the two personality dimensions.
- Cc. A negative correlation between the two dimensions is suggested by the darker areas of the scatterplot being in the lower regions of the neuroticism scroes.
- Dd. Although the correlation is not a straight line, there is some evidence for a curvilinear relationship.
- Aa. There seems to be a strong positive correlation between the two personality dimensions.
5353. For a small sample, the following is the PASW output for a scatterplot of negative affect versus scores on the Kessler-10 scale. Negative affect is a psychological construct relating to negative feelings and emotions. The Kessler-10 is a pre-diagnostic instrument that may indicate issues of psychological wellbeing. For the two variables, higher scores suggest more negative feelings and lower wellbeing, respectively.
- Aa. The scatterplot suggests a positive correlation. As negative affect decreases, scores on the Kessler-10 tend to decrease.
- Bb. The scatterplot suggests a negative correlation. As negative affect increases, scores on the Kessler-10 tend to decrease.
- Cc. The scatterplot suggests a positive correlation. As negative affect increases, scores on the Kessler-10 tend to decrease.
- Dd. The scatterplot suggests a negative correlation. As negative affect decreases, scores on the Kessler-10 tend to decrease.
- Aa. The scatterplot suggests a positive correlation. As negative affect decreases, scores on the Kessler-10 tend to decrease.
5454. A researcher finds a negative correlation between optimism and hours watching television. Which is the best inference?
- Aa. As television watching increases, people tend to become more optimistic.
- Bb. Too much television watching causes more pessimism.
- Cc. In order to cope with increased pessimism, people watch more television rather than undertake more physical activity.
- Dd. As optimism increases, people tend to spend less time watching television.
- Aa. As television watching increases, people tend to become more optimistic.
5555. A researcher finds a correlation of – 0.9 between two variables.
- Aa. The negative sign indicates that the relationship between the two variables is not close to a straight line.
- Bb. Because 0.9 is less than 1, the relationship between the two variables is not close to a straight line.
- Cc. Because – 0.9 is substantially less than 1, the relationship between the two variables is not close to a straight line.
- Dd. Because 0.9 is close to 1, the relationship between the two variables is close to a straight line.
- Aa. The negative sign indicates that the relationship between the two variables is not close to a straight line.
5656. The following is PASW output for a correlational analysis of the variables for the personality dimensions of extraversion and agreeableness.
- Aa. The correlation of 0.15 is less than the critical value 0.24, so the correlation is not significant. There is no evidence of a correlation in the population.
- Bb. The probability of a correlation of 0.15 is 0.24 which is not small, so do not reject the null hypothesis. There is no evidence of a correlation in the population.
- Cc. The correlation of 0.24 is not large and has a probability of 0.15, so reject the null hypothesis. There is evidence of a correlation in the population.
- Dd. The correlation of 0.15 is not large and has a probability of 0.24 and degrees of freedom 63. The correlation is not significant. There is no evidence of a correlation in the population.
- Aa. The correlation of 0.15 is less than the critical value 0.24, so the correlation is not significant. There is no evidence of a correlation in the population.
5757. From the study described in question 56, we may infer the following about the population:
- Aa. There is a positive correlation that is close to a straight line.
- Bb. There is a negative correlation that is close to a straight line.
- Cc. There is a weak positive correlation.
- Dd. We have no evidence for a correlation
- Aa. There is a positive correlation that is close to a straight line.
5858. When making inferences about correlations from samples to populations:
- Aa. the null hypothesis is that the population correlation is zero.
- Bb. the null hypothesis is that the sample correlation is zero.
- Cc. the null hypothesis is that the population mean correlation is zero.
- Dd. the null hypothesis is that the population correlation is one.
- Aa. the null hypothesis is that the population correlation is zero.
5959. We expect the following:
- Aa. the correlation between monthly salary and yearly salary will be 1. But if so a scatterplot is necessary to see whether the relationship is a straight line.
- Bb. the correlation between monthly salary and yearly salary will be 1/12. A scatterplot is necessary to see whether the relationship is a straight line.
- Cc. the correlation between monthly salary and yearly salary will be 1. If so the relationship is a straight line.
- Dd. the correlation between monthly salary and yearly salary will be less than 1. But, even so, the relationship is a straight line.
- Aa. the correlation between monthly salary and yearly salary will be 1. But if so a scatterplot is necessary to see whether the relationship is a straight line.
6060. Which makes the most sense?
- Aa. The Pearson correlation for the sample is represented by r and for the population by ρ (the Greek letter rho).
- Bb. The Spearman correlation for the sample is represented by r and for the population by ρ (the Greek letter rho).
- Cc. The Pearson correlation for the sample is represented by ρ (the Greek letter rho) and for the population by r.
- Dd. The sample statistic is ρ (the Greek letter rho) and the population parameter r.
- Aa. The Pearson correlation for the sample is represented by r and for the population by ρ (the Greek letter rho).
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- Quiz on QM, created by paulinomial on 04/11/2014.
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