Question 1

A two-independent sample t test is used when the same participants are observed in each of two groups.

Accepted Answer

A two-independent sample t test is used when two separate groups of participants are observed, not the same participants in each group.

Question 2

The critical values are reported with the test statistic of a one-sample and a two-independent sample t test using APA format.

Accepted Answer

While critical values are important when interpreting the results of t tests, they are not typically reported in APA format. Instead, the degrees of freedom and p-value are usually reported.

Question 3

Eta-squared is a more conservative estimate than omega-squared.

Accepted Answer

Eta-squared and omega-squared are effect size measures used in ANOVA. Eta-squared is generally considered a less conservative estimate than omega-squared as it tends to overestimate the true population effect size. Omega-squared is considered more conservative as it takes into account the variability of both the independent and dependent variables, whereas eta-squared only takes into account variability in the dependent variable.

Question 4

A researcher computes a two-independent sample t test and finds that the result is undefined (the denominator is equal to 0).This happens when there is no difference between the two groups .

Accepted Answer

The result of a two-independent sample t test being undefined (denominator equal to 0) can also occur when the standard deviation of one or both of the groups is equal to 0, which means the groups cannot be compared. It does not necessarily mean that there is no difference between the two groups.

Question 5

When scores are measured in a single group,the sample standard deviation is used as an estimate of the population standard deviation in the effect size formula for estimated Cohen's d.

Accepted Answer

This statement is true. When calculating Cohen's d for a single group, the sample standard deviation is used as an estimate of the population standard deviation.

Question 6

An estimated Cohen's d is interpreted in terms of the number of standard deviations that an effect shifted in some population.

Accepted Answer

Cohen's d is a standardized effect size that represents the difference between two means in terms of the number of standard deviations. It is commonly used in meta-analyses and other statistical analyses to compare the magnitude of effects across studies or groups.

Question 7

The p value is reported with the test statistic of a one-sample and a two-independent sample t test using APA format.

Accepted Answer

The p value is always reported with the test statistic of a one-sample and two-independent sample t test in APA format.

Question 8

When the difference between two groups is compared,the pooled standard deviation is used as an estimate for the population standard deviation in the effect size formula for estimated Cohen's d.

Accepted Answer

This statement is true. When calculating the effect size using Cohen's d, the pooled standard deviation is used as an estimate for the population standard deviation when comparing the difference between two groups.

Question 9

If d = 0.28,then we conclude that mean scores in some population have shifted 0.28 standard deviations below the mean.

Accepted Answer

The statement seems to refer to a value (d) that could be interpreted as a measure of effect size or difference, such as Cohen's d. However, Cohen's d specifically measures the difference between two means in terms of standard deviations, not a shift in the mean of a single population. Saying "mean scores in some population have shifted 0.28 standard deviations below the mean" misinterprets what such a statistic represents.

Question 10

A researcher reports the following result for a lower-tail critical test at a .05 level of significance,t(40)= 3.212,and accurately concludes that this result is significant.

Accepted Answer

The answer of A researcher reports the following result for...

Question 11

When the proportion of variance explained by some treatment is larger than 0.25,we conclude that there is a large effect in the population.

Accepted Answer

According to Cohen's guidelines, a proportion of variance explained (measured by effect size) greater than 0.25 indicates a large effect in the population.

Question 12

A researcher reports that the mean difference between two groups is 20.12 and the estimated standard error for the difference is 2.83.Hence,the conclusion must be to reject the null hypothesis for a two-tailed test at a .05 level of significance.

Accepted Answer

If the mean difference between two groups is significantly different from 0 (in this case, it is 20.12), and the estimated standard error for the difference is relatively small (in this case, it is 2.83), then we can conclude that the difference is statistically significant at a .05 level of significance, and therefore reject the null hypothesis.

Question 13

A two-independent sample t test is computed when mean differences are compared between two or more groups sampled from a population with an unknown variance.

Accepted Answer

A two-independent-sample t test is used for comparing mean differences between exactly two groups, not two or more groups, sampled from populations with unknown variances. For comparing means across more than two groups, an ANOVA is typically used.

Question 14

Cohen's d is a measure of proportion of variance.

Accepted Answer

Cohen's d is a measure of effect size, not proportion of variance.

Question 15

Measures of proportion of variance are computed the same for all t tests.

Accepted Answer

The answer of Measures of proportion of variance are computed...

Question 16

Effect size measures are an estimate of the size of an effect in a population.

Accepted Answer

Effect size measures are used to estimate the magnitude of an effect in a population and are not sample-dependent. They are used to provide a standardized way of comparing the strength of relationships or differences between groups.

Quiz 9: Testing Means: Two- Independent-Sample T Test With Confidence Intervals