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### When the population variance (or standard deviation) is unknown, it is impossible to use a z score for a hypothesis test.

True

### As sample size increases, the distribution of t statistics becomes flatter and more spread out.

False

### For any given value of (ALPHA), as df increases, the critical values in the t distribution table get smaller (move closer to zero).

True

### For a hypothesis test with a t statistic, the estimated standard error provides an approximation of the typical distance between a sample mean and the population mean.

True

### In general, the larger the value of the estimated standard error, the greater the likelihood of rejecting the null hypothesis.

False

### The boundaries for the critical regions in a two-tailed test using a t statistic with (ALPHA)=.05 will never get any closer to zero than +/-1.96.

True

### In general, a large value for a t statistic (one that is far from zero) is an indication that the sample data are not consistent with the null hypothesis.

True

### When doing an independent-samples t-test, it is usually necessary to compute the pooled variance before calculating the estimated standard error.

True

### For a hypothesis test with an independent-samples t, the larger the two sample variances are, the greater the likelihood that you will reject the null hypothesis.

False

### For an independent-samples t statistic, the standard error indicates the size of the difference that will typically be found between the two sample means if the null hypothesis is true.

True

### The results from an independent-samples t-test are reported as "t(14)=2.13, p > .05, two-tailed." For this test, the null hypothesis was rejected.

False

### For a research study comparing two treatment conditions, a related-samples design requires two scores for each participant, whereas an independent-samples design requires only one score for each participant.

True

### If df = n1 + n2 - 2 for the t statistic, then it is likely that the researcher was doing a related-samples study.

False

### A related-samples study and an independent-samples study both produce t statistics with df = 20. It can be concluded from this that the independent-samples study used more participants.

True

### The related-samples t statistic should be used in an experimental design that employs two different samples only if a matching variable has been used to pair, on a one-to-one basis, each participant in one sample with a participant in the other sample.

True

### If two treatments are both expected to produce a permanent or long-lasting change in the participants, then a repeated-measures design would not be appropriate for comparing the two treatments.

True

### Repeated-measures designs are particularly well suited to research studies examining learning or other changes that occur over time.

True

### F = 0.00 can be obtained only if all of the separate treatment means in the experiment are exactly the same.

True

### F = 1.00 implies that all of the separate treatment means in the experiment are exactly the same.

False

### If the F-ratio obtained from an ANOVA is great than the value listed in the table, the appropriate decision is to reject the null hypothesis.

True

### When the null hypothesis is true, the F-ratio is balanced so that the numerator and the denominator are both measuring the same source of variance.

True

### A research report presents the results of a one-factor ANOVA as follows: F(3,28) = 5.36, p < .01. The study must have compared three treatments.

False

### A research report presents the results of a one-factor ANOVA as follows: F(3,28) = 5.36, p < .01. The study must have used a total of 31 participants.

False

### If an ANOVA produces SSb = 20 and MSb = 10, then the study must be comparing two treatment conditions.

False

### All else being equal, the larger the difference between sample means, the larger the ANOVA F-ratio will be.

True

### When there are more than 2 treatment conditions in a study, some of the treatment means may not be significantly different from each other even if the null hypothesis is rejected by an ANOVA.

True

### If Ho is rejected by an ANOVA, follow-up statistical tests are not needed if the study has just 2 treatment conditions.

True

### Follow-up statistical tests are not needed if the decision is based on an ANOVA is to fail to reject the null hypothesis.

True

### An ANOVA is used to determine whether any significant differences exist at all among a set of treatment means, whereas follow-up statistical tests are used to determine exactly which means are significantly different from one another.

True

### With 3 treatment conditions, the alternative hypothesis for an ANOVA states that at least two of the three treatment means are different from each other.

True

### The F-ratio for an ANOVA comparing 3 treatment means with n=10 in each condition would have df = 2, 29

False

### In the F-ratio for a repeated-measures ANOVA, the variability that arises from individual differences among participants is always missing by design from the numerator, but must be computer and subtracted from the denominator.

True

### In a repeated-measures ANOVA, the variability that is due to differences among the treatment means will contribute to both the numerator and the denominator of the F-ratio.

False

### A repeated-measures study used a sample of n=8 participants to evaluate the mean differences between 2 treatment conditions. The ANOVA for this study will have dfe=7

True

### Based on the results of a repeated-measures ANOVA, a researcher reports F(4, 20) = 11.57, p < .01. For this ANOVA, the null hypothesis was rejected.

True

### Based on the results of a repeated-measures ANOVA, a researcher reports F(4, 20) = 11.57, p < .01. For this ANOVA, there were 4 treatment conditions.

False

### Tukey's HSD test may be used for post hoc tests with the repeated-measures ANOVA provided MSe is used instead of MSw.

True

### The repeated-measures ANOVA begins the same way as the independent-measures ANOVA, with the total variability partitioned into between-treatment and within-treatment components.

True

### If there are large individual differences for a particular dependent variable, a repeated-measures study is more likely to detect a treatment effect than an independent-measures study.

True