Q&A Hero

Q: 21 The notation produces the term we call thea) SSbetweenb) SSwithinc) SStotald) MSerror

Q: 20+ When we reject the null hypothesis in the analysis of variance we can conclude that a) all of the means are the same. b) all of the means are different. c) at least one of the means is different from at least one other mean. d) only one mean is different from one other mean.

Q: 19 If the null hypothesis in the analysis of variance were true, a) the variances would all be the same. b) the sample means would all be the same. c) the population means would all be the same. d) every subject would have the same score.

Q: 18 We use the symbol to represent a) the variance of the individual observations. b) the variance of the group totals. c) the variance of the error scores. d) the variance of the means.

Q: 17 When we use the phrase "within group" we mean a) the variability of the group means. b) the variability within the group means. c) the variability calculated for the scores within each group separately. d) the variability within all of the data points.

Q: 16 In the analysis of variance, MSerror is a) the average of the between group variances. b) the average of the between group sums of squares. c) the sum of squares of the within group variances. d) the average of the within group variances.

Q: 15+ In evaluating the F in the analysis of variance, we need to know a) the degrees of freedom for the Between Groups term. b) the degrees of freedom for the Within Groups term. c) only the total number of degrees of freedom. d) both a and b

Q: 14+ In the analysis of variance, the more the null hypothesis is false, a) the larger the value of F. b) the smaller the value of F. c) the smaller the value of the correlation coefficient. d) the harder it is to find a significant difference.

Q: 13 If the null hypothesis is true, we would expect the F in the analysis of variance to be a) approximately 0. b) somewhere around 1. c) at least 10. d) the value of F is not dependent on the status of the null hypothesis.

Q: 12+ The analysis of variance compares a) the total variance with the variance within groups. b) the total variance with the variance between group means. c) the variance between group means with the variance within groups. d) the variance within groups with the variance among all data points.

Q: 11+ When we speak about error variance in the analysis of variance we are speaking of a) differences between subjects in the same group. b) differences between subjects in different groups. c) the overall variability of scores in the experiment. d) the misrecording of data.

Q: 10 An important assumption in the one-way analysis of variance is that a) observations are random. b) observations are independent. c) subjects are related. d) there are equal numbers of observations in each group.

Q: 9 The analysis of variance assumes that a) the populations have no variance. b) the samples have equal variances. c) the populations have equal variances. d) the variances are normally distributed.

Q: 8 In the analysis of variance we will assume that a) the populations are normally distributed. b) the populations follow a rectangular distribution. c) the populations all have completely different shapes. d) There is no assumption about the shape of the population.

Q: 7+ If we had the following pattern of population means we would hope to conclude thata) the null hypothesis is false.b) the null hypothesis is true.c) the null hypothesis is confused.d) the experiment will have very little power.

Q: 6 If we want to have faith in the results of our particular study, we will be most concerned with a) random sampling. b) random assignment. c) equal sample sizes. d) a significant F.

Q: 5 Which of the following is not a critical element of the analysis of variance? a) the variance within each group b) the variance of the means c) the variance of the total sample d) the difference between the means

Q: 4 In the analysis of variance with three groups the null hypothesis is a) the three population means are all different. b) at least one of the population means is different from the others. c) the three population means are equal to each other. d) We can"t tell from what is given here.

Q: 3 In the Eysenck study of recall of lists of words, a significant F in the analysis of variance would at the least tell us that a) greater processing leads to greater recall. b) less processing leads to greater recall. c) the recall means are different in the different groups. d) none of the above

Q: 2 The major difference between t tests and the analysis of variance is that the latter a) deals with multiple groups. b) compares group variances. c) makes different assumptions about the populations from which we sample. d) none of the above

Q: 1+ The analysis of variance differs from a t test for two independent samples because a) thet test is limited to 2 samples. b) the analysis of variance can handle multiple samples. c) they test different hypotheses. d) both a and b

Q: 70 An English teacher believes his method of teaching grammar, usage, and mechanics is superior to most other approaches. He plans to compare his students' average score on a standardized test to the national average. Assuming a medium effect size, 20 students in his class, and = .05, what is power?

Q: 69 A student preparing to use a one-sample t-test indicated that power = .80 with a sample of 30. What was the effect size assuming = .05?

Q: 68 In a related samples t-test, husbands and wives scores are correlated such that r = .55, and the typical standard deviation on the measure of marital satisfaction is 5. Assuming the true mean difference in marital satisfaction is 5 points, the sample consists of 10 couples, and = .05:

Q: 67 The effect size for a one-sample t-test is .45. How many subjects would be necessary for the following levels of power if = .05?a) .40b) .80c) .95

Q: 66 The effect size for an independent samples t-test is .90. How many subjects would be necessary for the following levels of power if = .05?a) .50b) .77c) .91

Q: 65 A clinician is interested in examining the effectiveness of a new treatment approach for depression. Currently, 20 people are in the control group and 20 people are in the intervention group. In similar research, clinicians have reported post-treatment depression scores of 30 and 26 for the control groups and intervention groups respectively, and a standard deviation of 4.5. What is a reasonable estimate of power if = .05?

Q: 64 A pediatrician is interested in determining if cognitive development scores are lower for low-birthweight infants than normal infants. She has 49 active cases involving low-birthweight infants from who she could collect data to compare to the national average. What would power be assuming the following effect sizes and = .05?

Q: 63 Calculate effect size given the following information: a) = 3 b) = 10 c) = 5

Q: 62 Given the following values for , what is power assuming = .05?

Q: 61 Identify and explain three factors that affect power.

Q: 60 When comparing independent means, power is greater when sample sizes differ substantially.

Q: 59 When effect size is small, a small sample is typically sufficient to identify important differences.

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Q: 57 If power = .75, there is a 25% chance of correctly rejecting the null hypothesis.

Q: 56 According to statistical conventions, .80 is a small effect size.

Q: 55 Effect size is the difference between two population means divided by the sample size.

Q: 54 Power is higher when  is large than when  is small.

Q: 53 As sample size increases, so does power.

Q: 52 The larger the true difference between two means, the smaller power is.

Q: 51 Power is the probability of correctly rejecting the null hypothesis.

Q: 50 Suppose that in the previous question the 95% confidence interval on the difference between group means was computed to be . What can we conclude about the null hypothesis of no treatment effect?a) The difference is significant.b) The difference is not significant.c) The treatment doesn"t work.d) Confidence intervals are totally unsuited to this question.

Q: 49 We are trying to evaluate the efficacy of a treatment for claustrophobia. We have a control group that receives no treatment and an experimental group that receives our new treatment. We ask each participant to enter a very small room and stay there are long as they can. The mean of the control group is 10 seconds, with a standard deviation of 4 seconds. The mean of the experimental group is 18 seconds with a standard deviation of 8 seconds. Our best estimate of effect size is a) 2.0 b) 1.33 c) 1.0 d) 1.75

Q: 48 When we have two independent samples, a confidence limit is generally used to a) say something about the difference between population means. b) say something about the difference between sample means. c) specify the location of a single sample mean. d) refine the estimate of the variance.

Q: 47 In computing effect sizes for a comparison of independent samples, our measure may be less than ideal if a) we have heterogeneous variances and no control group. b) the data are decidedly nonnormal. c) we choose the wrong statistic for our denominator. d) all of the above

Q: 46 When we have two independent samples, the best measure of effect size a) will use a pooled standard deviation. b) might be based on the standard deviation of a control group. c) both a and b d) is best based on the standard deviation of the treatment group.

Q: 45 You are on an Institutional Animal Care and Use Committee (IACUC) at your school. You receive a request to run an animal study where the power will be .40. Do you approve the study? a) Yes; this indicates the study has a 60% chance of obtaining a significant result b) No; this indicates the study has only a 40% chance of obtaining a significant result. c) Yes; this indicates the results will be incorrect only 40% of the time. d) No; this indicates the study has a 40% chance of missing the significant result.

Q: 44 Which of the following determines how the power of an experiment varies with sample size?

Q: 43 What is the easiest way to increase power? a) redesign the study b) decrease the error variance c) use different statistical tests d) increase the sample size

Q: 42 The probability of rejecting a false null hypothesis is called a) statistics. b) power. c) statistical significance. d) prediction.

Q: 41 Power is defined as the a) probability of rejecting a true null hypothesis. b) probability of accepting a false null hypothesis. c) probability of rejecting a false null hypothesis. d) Power is not related directly to the null hypothesis.

Q: 40 Which of the following reduces power? a) large differences between the means b) large levels of variability c) large sample sizes d) large alpha levels

Q: 39 Power is controlled by a) sample size. b) variability. c) true mean differences. d) all of the above

Q: 38 Suppose a weak relationship exists between fetal alcohol syndrome and hyperactivity. We are more likely to find that the sample correlation is significant if a) we have a small sample. b) we have a very large sample. c) it doesn't depend at all on sample size. d) we choose people who have all experienced fetal alcohol syndrome.

Q: 37 Which of the following is a reason why we may NOT find a significant difference between two groups? a) The sample size was too small. b) The true mean difference between the groups was too small. c) The variability between the groups was too large. d) all of the above

Q: 36 Before running an experiment, we decide to calculate power. We find that the power equals .95. What conclusion can be made? a) We should not run the experiment. b) 95% of the time, we will not find a significant result. c) We should run the experiment. d) We need 95 subjects to find a significant result.

Q: 35+ A harmonic mean can be used to calculate the mean sample size for unequal sample sizes. For _______ the values of k would equal _______. a) related means t test; 3 b) related means t test; 2 c) independent samples t test; 3 d) independent samples t test; 2

Q: 34+ What is the power in an experiment with two independent groups when the null hypothesis is true? a) .05 b) .95 c) .05/2 d) undefined

Q: 33+ It is important to think about power because a) everyone uses it. b) we don"t want to run experiments that have little chance of finding something. c) it is likely to guarantee successful studies. d) none of the above

Q: 32 Do you have more power with a one-tailed test or a two-tailed test? a) We have more power with a two-tailed test. b) We have more power with a one-tailed test. c) It depends on which alternative hypothesis is true. d) Beats me!

Q: 31 Is it more meaningful to find a significant difference with a relatively small sample size or with a relatively large sample size? a) It points to a more robust difference if you have a small sample size. b) It points to a more substantial difference if you have a large sample size. c) The two differences are equally substantial. d) We can"t tell anything about the size of the effect from what is given here.

Q: 30 It is easy to forget whether the entry in the table at the back of the book represents power or 1 " power. (Different tables do it differently). We can easily figure it out, however. If the entry is power, we would expecta) the values to decrease as we move down the table to larger sample sizes.b) the values to decrease as we move across to higher levels of .c) the values to increase as we move to the left to smaller .d) the values to increase as we move down the table to higher levels of .

Q: 29+ If we have two related samples, the power of our test will a) increase as the correlation between the two samples increases. b) increase as the correlation between the two samples decreases. c) be at its maximum when the correlation is zero. d) The correlation is irrelevant.

Q: 28 If we have the possibility of different numbers of subjects in each of two groups, we will maximize our power if a) we put most of the subjects in the group we care most about. b) we put most of the subjects in the group we care least about. c) we equalize the number of subjects in the two groups. d) it doesn"t make any difference so long as we maximize the total number of subjects overall.

Q: 27 When we have two independent groups with different numbers of subjects in the two groups, and we want to calculate the power we have for a given set of values of and , the N that we will use in our calculations isa) the number of subjects in the smaller group.b) the number of subjects in the larger group.c) the harmonic mean of the two sample sizes.d) the total number of subjects in our experiment.

Q: 26+ If we use the standard approach to solve for the necessary sample size so that we have power = .75 in a two-sample t test, the value of N that we will obtain from the tables is a) the number of subjects we will need in each group. b) the number of subjects we will need overall. c) the number of groups we will need to run. d) the number of subjects that we actually have available.

Q: 25+ The text suggested that the highest value of power that we are likely to be able to afford, assuming that we don"t have huge differences between our groups, is something like a) .20 b) .50 c) .80 d) .95

Q: 24 The formula allows us to calculate a) the power for a t test for two independent samples. b) the power for a t test for one sample. c) the power for a test on a correlation coefficient. d) any kind of power we need.

Q: 23 In calculating power we separate the effect size and the sample size. We do this so that a) we can look up the sample size needed for a specific effect size and power. b) we can calculate the effect size we need for a given power. c) we can avoid ever having to deal with the sample size. d) we can confuse everyone.

Q: 22 When we use the notation "f(N)" we are denoting a) some function of the sample size. b) a fraction of N. c) the number of observations in the full sample. d) the symbol for power.

Q: 21+ In calculating power we calculate a statistic called delta (). This statistic is a) the effect size. b) the sample size. c) a combination of the effect size and the sample size. d) the difference we are looking for.

Q: 20 Which of the following represents Cohen's rough estimates of small, medium, and large effect sizes? a) .20, .50, .80 b) .00, .50, 1.50 c) .80, .50, .20 d) .40, .50, .60

Q: 19+ When people such as Cohen say that a medium size difference has an effect size of .50, they are saying that we want the power for a) one mean being half as large as another. b) one mean being half a point larger than another. c) one mean being half a standard deviation larger than another. d) one mean being based on half as many subjects as the other.

Q: 18 When it comes to estimating the effect size we can use a) prior research. b) a statement of what we would expect to find. c) special conventions that have been set by others. d) all of the above

Q: 17+ The effect size can best be thought of as a) the size of the largest mean. b) the size of the difference between means in standard deviation units. c) a mixture of the size of the difference and the sample size. d) the largest difference we would hope to find.

Q: 16+ The effect size is generally denoted by

Q: 15 The effect size () is a) the size of the difference between the means. b) the size of the samples you use. c) the difference between the means scaled by the size of the standard deviation. d) the width of the distribution.

Q: 14+ To increase power, the easiest variable to control in designing an experiment is usually a) the difference between the population means. b) the sample size. c) the sample standard deviation. d) the shape of the distribution.

Q: 13 When we calculate the power for a number of different experimental designs, the calculations depend upon a) a different approach to each kind of problem. b) the way we use the tables. c) the number of subjects we have. d) the same basic steps, but with slightly different formulae.

Q: 12+ If we have two experiments in which the groups are equally different, the one with the larger power will be the one thata) had the larger sample size.b) had the smaller level of .c) had the larger d) assigned subjects randomly.