Deck 12: The T-Test

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Question
When both variables are categorical, the t-test should be used for hypothesis testing.
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Question
The term parametric statistics refers to tests that make assumptions about the distribution of data.
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Recoding continuous variables as categorical variables is discouraged because it results in a loss of information.
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The t-test has four test assumptions.
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The critical values of the t-test are provided by Student's t-test distribution.
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One-tailed tests are used most often, unless compelling a priori knowledge exists or it is known that one group cannot have a larger mean than the other.
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A test for the equality of variances is the Levene's test.
Question
The term robust is used, generally, to describe the extent to which test conclusions are unaffected by departures from test assumptions.
Question
A combination of visual inspection and statistical testing should always be used to determine normality.
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The Kolmogorov-Smirnov test is a test of equal variance.
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Nonnormality is sometimes overcome through variable transformation.
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When problems of nonnormality cannot be resolved adequately, analysts should consider nonparametric alternatives to the t-test.
Question
Analysts should always examine the robustness of their findings.
Question
All t-tests first test for equality of means and then test for equality of variances.
Question
The paired samples t-test tests the null hypothesis that the mean difference between the before and after test scores is zero.
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A paired t-test violates the assumption of homogeneity.
Question
The one-sample t-test tests whether the mean of a single variable is different from a prespecified value (norm).
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The Mann-Whitney and Wilcoxon tests are equivalent.
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The Mann-Whitney and Wilcoxon tests assign ranks to the testing variable and test whether the sums of ranks differ between the two categories.
Question
The signed rank test is an independent samples test that examines differences of mean ranks to evaluate whether two samples come from the same population.
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Deck 12: The T-Test
1
When both variables are categorical, the t-test should be used for hypothesis testing.
False
2
The term parametric statistics refers to tests that make assumptions about the distribution of data.
True
3
Recoding continuous variables as categorical variables is discouraged because it results in a loss of information.
True
4
The t-test has four test assumptions.
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5
The critical values of the t-test are provided by Student's t-test distribution.
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6
One-tailed tests are used most often, unless compelling a priori knowledge exists or it is known that one group cannot have a larger mean than the other.
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7
A test for the equality of variances is the Levene's test.
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8
The term robust is used, generally, to describe the extent to which test conclusions are unaffected by departures from test assumptions.
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9
A combination of visual inspection and statistical testing should always be used to determine normality.
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10
The Kolmogorov-Smirnov test is a test of equal variance.
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11
Nonnormality is sometimes overcome through variable transformation.
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12
When problems of nonnormality cannot be resolved adequately, analysts should consider nonparametric alternatives to the t-test.
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13
Analysts should always examine the robustness of their findings.
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14
All t-tests first test for equality of means and then test for equality of variances.
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15
The paired samples t-test tests the null hypothesis that the mean difference between the before and after test scores is zero.
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16
A paired t-test violates the assumption of homogeneity.
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17
The one-sample t-test tests whether the mean of a single variable is different from a prespecified value (norm).
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18
The Mann-Whitney and Wilcoxon tests are equivalent.
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19
The Mann-Whitney and Wilcoxon tests assign ranks to the testing variable and test whether the sums of ranks differ between the two categories.
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20
The signed rank test is an independent samples test that examines differences of mean ranks to evaluate whether two samples come from the same population.
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