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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

The larger the value of df, the more a t distribution resembles a normal distribution.

True

The t distribution is symmetrical and has a mean of zero.

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

As sample size increases, the estimated standard error also increases.

False

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-ratios are always greater than or equal to zero.

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 null hypothesis is true, the F-ratio is expected to have a value near 1.00.

True

The sampling distribution of F-ratios is negatively skewed.

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

In an ANOVA, SSb is a measure of how much the treatment means differ from one another.

True

In an ANOVA, MSt will always equal the sum of MSb and MSw.

False

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

If an ANOVA produces SSb = 20 and SSw = 40, then eta sq. = 0.50.

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

For a repeated-measures ANOVA, MSe = MSw - MSb-Ss

False

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

The effect sized statistic that is appropriate for a repeated-measures analysis is computed in such a way as to take account of the fact that variance due to person cannot possibly affect the value of MSb.

True