Q&A Hero

Q: 38+ A newspaper headline writer found that the more adjectives she put in the titles of her articles, the greater the number of newspapers that were sold that day. This relationship between numbers of adjectives and newspaper sales must be a) significantly positive. b) significantly negative. c) monotonic. d) positive.

Q: 37+ Which of the following pairs go together? a) dependent variable : criterion variable b) dependent variable : predictor variable c) independent variable : criterion variable d) independent variable : Y variable

Q: 36 If one of our variables is a dichotomy, the correlation we compute is a) Spearman's rS. b) a rank correlation. c) a point-biserial correlation. d) a tetrachoric correlation.

Q: 35 An intercorrelation matrix is one that a) presents the correlations of each variable with each other variable. b) is symmetric. c) has as many rows as it does columns. d) all of the above

Q: 34 In testing the significance of a correlation coefficient, the degrees of freedom area) Nb) N - 1c) N - 2d) N - 3

Q: 33 The correlation in the population is denoted by

Q: 32 When we say that a correlation coefficient is statistically significant, we mean that a) we have reason to believe that it reflects an important relationship between variables. b) we have reason to believe that the relationship is positive. c) we have reason to believe that the true correlation in the population is not 0.0. d) we have strong support for a causal statement about the relationship.

Q: 31+ Which of the following is NOT a reason to explain why infant mortality increased with the number of physicians? a) More physicians would lead to greater rates of reporting infant deaths. b) Physicians go where the problems are. c) Both variables are under the control of some third variable. d) All of these answers are possible.

Q: 30+ If we look at the correlation between college admissions test scores and subsequent performance in college for all admitted applicants, we are likely to a) underestimate the degree of correlation between test score and potential performance. b) overestimate the relationship. c) do quite a good job of estimating the relationship if our sample is sufficiently large. d) not know much more than when we began.

Q: 29 When we have a relationship that is continually rising, but the line showing the relationship is not necessarily straight, we call this a _______ relationship. a) linear b) reclining c) bivariate d) monotonic

Q: 28+ When the data are in the form of ranks we a) need a special formula that Spearman derived. b) can just apply the standard formula to the ranks. c) need to convert the ranks to raw scores before computing the correlation. d) cannot do anything with the data.

Q: 27 If the correlation between two variables is .76, and the sample size is large, we can conclude that a) there is 76% of a relationship between the two variables. b) there is a strong positive relationship between the two variables. c) there is no relationship between the two variables. d) both a and b

Q: 26+ For the following data, XY is equal toX 2 4 5Y 2 3 4a) 11b) 9c) 99d) 36

Q: 25 Which of the following is the formula for the covariance? a) b) c) d)

Q: 24+ If high scores on X are paired with low scores on Y, the covariance is going to be a) positive. b) negative. c) zero. d) There is no way to tell.

Q: 23+ For a given set of data the covariance between X and Y is .80. The standard deviation of X is 2.0, and the standard deviation of Y is 3.0. The resulting correlation is closest to a) .00 b) .15 c) .80 d) -.30

Q: 22 The covariance will always a) be a positive number. b) be larger than the variance. c) *reflect the direction of the relationship. d) be less than 1.0.

Q: 21 We can often use a Pearson correlation even when a relationship is curvilinear. This is because a) *a straight line will often fit the data remarkably well. b) you can always calculate a correlation with any set of data. c) the correlation coefficient doesn"t care if the relationship is curvilinear. d) there is no alternative.

Q: 20 A curvilinear relationship is one in which a) one variable always increases as the other increases. b) as X increases, Y will increase and then level off or fall. c) as X increases, Y will decrease and then level off or rise. d) both b and c

Q: 19 The example showing a negative relationship between speed and accuracy tells us that a) the slowest responders are always the most accurate. b) the fastest responders are always the most accurate. c) on average, slower responders are more accurate than faster responders. d) speed is a virtue.

Q: 18+ Which of the following represents a closer relationship between two variables? a) r = .00 b) r = .50 c) r = -.30 d) r = -.65

Q: 17+ A reliable correlation is one that a) is significantly different from 0. b) is likely to be closely approximated in a future study. c) is close to 1.00. d) is non-negative.

Q: 16 In the scatterplot for the data in the previous question, the biggest outliers are likely to be a) Portugal and Sweden. b) Japan and Ireland . c) Denmark and Sweden. d) the U.S.A. and West Germany.

Q: 15+ Inglehart (1990) presented data on the relationship between income (as represented by a country's Gross National Product), and reported Satisfaction With Life for 24 countries. These data speak to the issue of whether people in countries with a higher standard of living also report greater satisfaction. The data have been sorted by Satisfaction. Country Satisf GNP Country Satisf GNP Portugal 5.5 1,900 Canada 7.2 13,300 Greece 5.8 3,800 Belgium 7.3 9,100 Japan 6.4 10,700 Britain 7.5 9,000 Spain 6.5 4,200 U.S.A. 7.55 15,700 Italy 6.5 6,300 Ireland 7.7 5,000 South Africa 6.6 2,100 Luxemburg 7.75 9,400 France 6.6 9,900 Finland 7.75 10,700 Argentina 6.72 2,200 Norway 7.85 14,000 Hungary 6.95 4,300 Australia 7.9 10,100 Austria 7.1 9,300 Switzerland 7.95 15,900 Netherlands 7.2 9,300 Denmark 8.0 11,000 W. Germany 7.2 11,000 Sweden 8.0 11,900 Visual inspection of these data would suggest that the correlation is closest to a) .00 b) .50 c) .90 d) -.50

Q: 14 The data illustrated in the graph below suggest a) that there is a strong positive relationship between height and weight. b) that there is no relationship between height and weight. c) that some other variable is involved in the relationship. d) that these data are unreliable.

Q: 13 The difference between a point biserial coefficient and a normal Pearson correlation coefficient is that a) a point biserial correlation is based on two continuous variables. b) a point biserial correlation is based on one dichotomous variable and one continuous variable. c) a point biserial correlation is based on two dichotomous variables. d) the kind of variable has nothing to do with the issue.

Q: 12 A dichotomous variable is one that a) can take on any number of values. b) can take on one of only three values. c) can take on one of only two values. d) can take on only one value.

Q: 11+ If the correlation between the rating of cookie quality and cookie price is .30, and the critical value from the table of significance of correlation coefficients is .35, we would say that a) the correlation is not significant. b) the correlation is significant. c) the difference is too close to call. d) we don"t have any way to come to a conclusion.

Q: 10 When we say that the correlation between Age and test Performance is significant, we mean a) there is an important relationship between Age and Performance. b) the true correlation between Age and Performance in the population is equal to 0. c) the true correlation between Age and Performance in the population is not equal to 0. d) getting older causes you to do poorly on tests.

Q: 9 When we use heterogeneous subsamples of data, such as older and younger subjects, the resulting correlation between intelligence and education could a) tell you more about the relationship between Age and Education than between Intelligence and Education. b) be very misleading. c) represent the relationship between Education and Intelligence accurately. d) all of the above

Q: 8 When we restrict the range of X or Y, we may a) lower the correlation from what it would otherwise be. b) raise the correlation from what it might be. c) leave the correlation the same as it would otherwise be. d) All of the above are possible.

Q: 7 Spearman's correlation coefficient (rS ) applies to a) any data. b) linear data. c) data that have been converted to ranks. d) only continuous data.

Q: 6+ The correlation between two variables is defined as a) the covariance of those variables divided by the product of their standard deviations. b) the covariance of those variables divided by the variance of X. c) the covariance of those variables divided by the variance of Y. d) the cross-product of all of the pairs of scores.

Q: 5+ Early in the correlation chapter the author showed figures in which he drew vertical and horizontal lines at the mean of each variable to cut the graph into four quadrants. When there is a high positive correlation between two variables, we would expect most of the data points to fall a) to the right of vertical line. b) in the upper right and lower left quadrants. c) below the horizontal line. d) equally in all four quadrants.

Q: 4 The correlation between two variables is a measure of the degree to which a) points cluster together around some best-fitting straight line. b) differences in one variable can be predicted from differences in the other variable. c) one variable varies with the other variable. d) all of the above

Q: 3 In the previous question, a "best-fitting" line drawn through the data points would most likely go a) straight across the page. b) straight down the page. c) from upper left to lower right. d) from lower left to upper right.

Q: 2 The following is a scatterplot of data that my students collected concerning the relationship between the cost of chocolate chip cookies and their rated quality. The correlation between the two variables is most likely to be a) -.50 b) -.80 c) .80 d) .00

Q: 1+ In plotting the relationship between the incidence of breast cancer and the level of vitamin D in the body, we would most likely plot a) vitamin D on the Y axis and incidence of breast cancer on the X axis. b) average daily sunlight on the Y axis and the incidence of breast cancer on the X axis. c) vitamin D on the X axis and incidence of breast cancer on the Y axis. d) The three answers above are equally good.

Q: 69 Explain how a critical value is used to test hypotheses.

Q: 68 A research article stated that there was a mean difference in depressive symptoms between male and female clients. The p value was .075. What does this mean?

Q: 67 Another student concluded that James was in the study skills class based on his exam score, but in fact he was not. What type of error did the student make?

Q: 66 The average test score of individuals in the study skills group is 80 and the standard deviation is 7.5. Jessica got a 65. Do you think Jessica was in the study skills group? Explain.

Q: 65 Given the following hypotheses, is a one-tailed or two-tailed test more appropriate?

Q: 64 A researcher has calculated power as .40. a) What does this mean? b) What is the probability that this researcher will make a Type II error if the null hypothesis is false? Explain.

Q: 63 Given the following p values, would you reject or retain the null hypothesis if you want to be 95% confident that you are not making a Type I error. a) p = .05 b) p = 1.0 c) p = .10 d) p = .025 e) p = .075

Q: 62 Give an example of a hypothesis that would be appropriate for testing with a two-tailed test.

Q: 61 Give an example of a hypothesis that would be appropriate for testing with a one-tailed test.

Q: 60 A child psychologist is interested in determining if a new type of cognitive therapy will reduce behavior problems among children with ADHD more than Ritalin will in another group of children with ADHD. a) Write an appropriate null hypothesis. b) Write an appropriate research hypothesis.

Q: 59 When testing a hypothesis, we normally retain the null hypothesis when the test statistic is smaller than the critical value.

Q: 58 When the direction of difference between the sample mean and the population mean is not specified, a two-tailed test is appropriate.

Q: 57 If the alternative hypothesis in a study is H0 > 0, a one-tailed test is called for.

Q: 56 If .05 is the rejection level, we would reject the null hypothesis if the probability of the test statistic, given that the null hypothesis is true, was .07.

Q: 55 A standard error is the value of a statistic at or beyond which the null hypothesis is rejected.

Q: 54 The probability of making a Type I error is unrelated to the probability of making a Type II error.

Q: 53 Power is the probability of making a Type II error.

Q: 52 Alpha is the probability of making a Type I error.

Q: 51 Type II error is retaining the null hypothesis when it is true.

Q: 50 Type I error is rejecting the null hypothesis when it is true.

Q: 49 Another name for sampling error is a) variability due to chance. b) error variance. c) constancy. d) both a and b

Q: 48 A Type I error has occurred if we a) reject a null hypothesis that is really false. b) retain a null hypothesis that is really false. c) retain a null hypothesis that is really true. d) reject a null hypothesis that is really true.

Q: 47 A null hypothesis is rejected when a) the differences are due to sampling error. b) the probability of finding a difference that large if the population means are equal is very low. c) the probability of finding a difference that large if the population means are equal is very high. d) the distribution is not normal.

Q: 46 The probability of NOT rejecting a null hypothesis when it is false is called? a) a Type I error b) a Type II error c) experimenter error d) method error

Q: 45 The _______ assumes all means are equal for a given measure? a) alternative hypothesis b) random hypothesis c) predicted hypothesis d) null hypothesis

Q: 44 The probability of NOT rejecting a FALSE null hypothesis is also known asa) Type II Errorb) Type I Errorc) alpha d) both b and c

Q: 43 Rejecting a true null hypothesis is known asa) Type II Errorb) Type I Errorc) alpha d) both b and c

Q: 42 After running a t-test on the mean numbers of jelly beans that men and women eat over the course of the year, I conclude that men eat significantly more jelly beans than women. If men and women actually eat the same number of jelly beans, my conclusion is a) a valid conclusion b) a Type I error c) a Type II error d) an example of power

Q: 41 A Type I error concerns a) the probability of rejecting a true null hypothesis. b) the probability of rejecting a false null hypothesis. c) the probability of not rejecting a true null hypothesis. d) the probability of not rejecting a false null hypothesis.

Q: 40 The null hypothesis is the statement that a) population means are equal. b) population means differ between groups. c) it is the hypothesis you generally hope to prove. d) exciting things are going on.

Q: 39+ Dr. Harmon expected that her neurotic patients would come significantly earlier to all scheduled appointments compared to other patients, and planned to run a one-tailed test to see if their arrival times were much earlier. Unfortunately, she found the opposite result: the neurotic patients came to appointments later than other patients. What can Dr. Harmon conclude from her one-tailed test? a) Neurotic patients came to appointments significantly later than other patients. b) Neurotic patients came to appointments significantly earlier than other patients. c) Non-neurotic patients came to appointments significantly earlier than neurotic patients. d) Neurotic patients did not come to appointments significantly earlier than other patients.

Q: 38+ A researcher was interested in seeing if males or females in large lecture classes fell asleep more during in-class videos. The null hypothesis of this study is a) males will fall asleep more than females. b) females will fall asleep more than males. c) males and females fall asleep at the same rate. d) More information is needed.

Q: 37 The value of the test statistic that would lead us to reject the null hypothesis is called a) the critical value. b) the test value. c) the rejection value. d) the acceptance value.

Q: 36+ If we erroneously conclude that motorists are more likely to honk at low status cars than high status cars, we a) have made a Type I error. b) have made a Type II error. c) would have made that conclusion 5% of the time if the null hypothesis were true. d) both a and c

Q: 35+ Another name for a one-tailed test is a a) directional test. b) non-directional test. c) uniform test. d) specific test.

Q: 34 A two-tailed test is _______ powerful than a one-tailed test if we are sure the difference is in the direction that we would have predicted. a) more b) less c) equally d) We cannot tell.

Q: 33+ When we are willing to reject the null hypothesis for any extreme outcome, we are making a a) two-tailed test. b) one-tailed test. c) singular test. d) omnibus test.

Q: 32 We would like to a) maximize the power of a test. b) minimize the probability of a Type I error. c) do both a and b d) maximize the probability of a Type II error.

Q: 31+ Which of the following pairings is correct?a) Type I; Type II:: b) Type I; Type II :: c) Type I; Type II :: d) Type I; Type II ::

Q: 30 A Type II error refers to a) rejecting a true null hypothesis. b) rejecting a false null hypothesis. c) failing to reject a true null hypothesis. d) failing to reject a false null hypothesis.

Q: 29 Sometimes we reject the null hypothesis when it is true. This is technically referred to as a) a Type I error. b) a Type II error. c) a mistake. d) good fortune.

Q: 28 The area that encompasses the extreme 5% of a distribution is frequently referred to as a) the retention region. b) the rejection region c) the decision region. d) none of the above